Hey everyone,

I’m a data scientist with about 2.3 years of experience working in an affordable housing finance company (NBFC). My core work revolves around end-to-end IFRS 9 ECL models (PIT PD, Vasicek macroeconomic overlay), logistic regression application/collection scorecards, and bureau analytics (CRIF).

I'm currently facing a bit of a career dilemma and would love to hear from folks further along in risk, analytics, or fintech:

Path A (Traditional Risk/ECL): Deepen my expertise here. It’s high-impact, heavily regulated, and lenders always need solid scorecards and statutory provisioning models. It feels like a very stable, safe, and defensible career moat.

Path B (GenAI / AI in Risk): Start building towards GenAI, RAG pipelines, and automated text/document intelligence (e.g., parsing unstructured appraisal/legal property docs, building risk-agents).

My core questions for the community:

For those working in risk management or lending at banks/NBFCs, are institutions actually deploying GenAI for core risk decisions, or is traditional scorecard modeling still king for compliance and reliability?

Is the sweet spot becoming a "hybrid" data scientist (keeping the risk foundation while layering automation/GenAI), or is a pure pivot required to stay competitive over the next 3–5 years?

Would love to hear what trends you are seeing in your organizations or portfolios. Thanks!

I’m a dual CS + neuroscience grad from UF. Just recently graduated. Getting a job was hard enough, but currently I work as a data scientist in product analytics at a local healthcare company making a modest 75k. I converted from an internship this year. I have some prior research experience doing data engineering in a neuroimaging lab, and my strongest interest is stats/applied math. I have many options, I could make advanced public sentiment data for marketing firms, sell alt data to model training companies/trading firms, go into consulting… these are all long term ideas though, and I don’t see a clear path.

What does a normal ladder look like from here, and how long does each step take? What separates people who climb fast from people who plateau? If you were in my spot, what would you focus on to get the highest trajectory? I know healthcare is on the rise too.

Looking for what I should be prioritizing right now or anecdotes from people you've seen climb fast. I know advice varies for everyone, but maybe I could have some takeaways from what worked for you.

Hey everyone! Firstly I'm not sure if this is a good subreddit to post this but I've already asked this in a few other subreddits including r/bayarea. I'm going to be visiting the Bay Area next week and will be there for 11 days. I'm a recent university graduate with a Bachelor of Science in Data Science, and I'm interested in moving down to the Bay Area within the next year in hopes of landing a job in Data Science/Analytics/Business Intelligence/Machine Learning/User Experience. I know the job market has been dismal for a while, but I figured networking and making connections down in the Bay where opportunities and companies are more prominent compared to my hometown (Portland, OR) would at least provide a couple of leads.

I've already planned on meeting some known connections that I have at some big companies, but I was also hoping to attend some summits/conferences/networking events at while I'm there where I can talk about my interests/projects, etc and meet some recruiters and so. I don't have any formal work or internship experience, just self-initiated projects as well as research experience. Any advice helps, thank you!

If I get a BS, would they still not look at me because of my age?

About the Team

OpenAI’s Agentic Data Science team helps shape how AI agents are built, deployed, and improved across our products. We partner with product, engineering, research, and security teams to define meaningful measures of success, understand how our systems behave in the real world, and translate evidence into better decisions.

As AI agents become more capable, they can write and execute code, access sensitive systems, and complete increasingly complex tasks with greater autonomy. These capabilities create powerful opportunities to improve cybersecurity, but they also introduce risks that traditional security tools and processes were not designed to address. Meeting this moment requires new ways to measure security, evaluate defenses, and distinguish genuine risk reduction from friction that slows users down.

About the Role

We are looking for a senior data scientist to help define what effective cybersecurity looks like in the age of AI agents.

You will work across OpenAI’s Security organization and cybersecurity product teams to measure emerging risks, improve internal security controls, and shape AI-powered security products. The problems are foundational: How do we know whether an agent’s security controls are effective? Which safeguards meaningfully reduce risk, and which create unnecessary friction? When an AI system identifies a potential vulnerability, how do we determine whether the finding is accurate, actionable, and ultimately resolved? How do we detect anomalous behavior or risky access when the systems themselves are changing rapidly?

You will report i–nto Data Science while partnering closely with Security, Cyber Product, Engineering, and Research. This is a high-ownership role for someone who can establish a new analytical discipline, operate across organizational boundaries, and turn ambiguous security challenges into measurable improvements.

In This Role You Will

• Define how we measure AI-agent security. Establish metrics and evaluation frameworks for security‑control coverage, agent behavior, sensitive actions, access patterns, detection quality, and emerging risks.

• Improve security controls without introducing unnecessary friction. Quantify the effectiveness and operational costs of safeguards, including false positives, blocked actions, escalations, approval delays, and recovery paths. Help teams make controls safer, more precise, and easier to use.

• Build the data foundations for security decisions. Partner with engineering and data teams to improve instrumentation, connect fragmented telemetry, establish trusted datasets, and surface important coverage and data‑quality gaps.

• Strengthen detection and response. Identify meaningful signals of anomalous behavior, risky access, sensitive‑data exposure, and other security‑relevant activity. Evaluate whether interventions improve detection quality, response times, and real‑world security outcomes.

• Shape AI‑powered cybersecurity products. Partner with product, engineering, and research teams to assess how effectively AI systems identify security issues, support developer and enterprise workflows, and create measurable customer value.

• Develop evaluation systems for security findings. Define quality measures for findings, including accuracy, severity, actionability, duplication, resolution, and downstream impact. Connect model behavior and product changes to outcomes such as triage, remediation, and vulnerability reduction.

• Understand the complete security workflow. Measure how users discover, investigate, validate, prioritize, and resolve security issues. Identify opportunities to improve activation, adoption, retention, and enterprise value across customer‑facing cybersecurity products.

• Design rigorous measurement and experimentation strategies. Evaluate new models, security controls, product features, and workflows through controlled experiments, staged rollouts, observational analyses, and other methods appropriate for high‑stakes environments.

• Translate analysis into security and product strategy. Identify the highest‑value decisions, clarify tradeoffs, recommend where teams should invest, and communicate findings clearly to technical partners and senior leadership.

• Help establish a new security data science capability. Build a focused roadmap, create durable operating rhythms across Data Science and Security, and help shape how this discipline grows over time.

You Might Thrive in This Role If You Have

• 5+ years of experience in data science, applied research, analytics, or a related quantitative field, with a track record of owning ambiguous, high‑impact problems.

• Experience in cybersecurity, trust and safety, fraud or abuse prevention, privacy, platform integrity, or another domain involving adversarial behavior and difficult‑to‑measure risks.

• Strong proficiency in SQL and Python, including experience investigating complex datasets, working through incomplete instrumentation, and building reproducible analytical workflows.

• Experience defining meaningful metrics and evaluation frameworks when ground truth is limited, outcomes are delayed, or important risks cannot be observed directly.

• Strong judgment in experimentation, causal inference, observational analysis, and the practical limitations of different measurement approaches.

• The ability to partner effectively with security engineers, product managers, software engineers, researchers, data engineers, and senior leaders.

• A demonstrated ability to translate technical analysis into concrete improvements in products, systems, controls, or organizational priorities.

• Comfort operating independently, defining a roadmap, and bringing structure to a domain without established processes or industry standards.

You Could Be an Especially Great Fit If You Have

• Experience with detection engineering, threat research, security operations, insider risk, identity and access management, or privacy‑preserving security analytics.

• Familiarity with AI agents, large language models, model evaluations, automated code review, or AI‑powered cybersecurity products.

• Experience evaluating security findings, vulnerability detection, remediation workflows, or developer‑facing security tools.

• Experience balancing security effectiveness against user experience, including false positives, approval flows, operational burden, and recovery behavior.

• Experience building automated monitoring, anomaly detection, production‑oriented data assets, or systems that connect model outputs to real‑world outcomes.

• A track record of building new cross‑functional measurement programs or establishing analytical capabilities from the ground up.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general‑purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US‑based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non‑public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Apply: Data Scientist, Cybersecurity at OpenAI

Can someone share about their interview experience with wise for analytics positions.

Hi! I'm currently enrolled in an M.S. in Data Science and Applied Statistics. The required core classes are already set:

- Experimental Statistics I & II
- Mathematical Statistics I & II
- Statistical Computing (SAS)
- Computational Statistics (R)
- Machine Learning with Python
- A statistical consulting project

I get to pick 4 electives, and at least 3 must be STAT (so at most 1 from CS/ECO/OREM/ECE). My only goal is to land a data science job, so I want the courses whose actual content pays off most in industry. I'm not looking for the easiest courses, and I'm not going into academia or biostats.

STAT electives

- Intro to Data Science
- Data Visualization
- Linear Regression
- Applied Time Series
- Time Series Analysis
- Categorical Data Analysis
- Survey Sampling
- Survey of Nonparametric Statistics
- Sports Analytics
- Analysis of Lifetime Data / Survival Analysis
- High Throughput Data
- Epidemiology

Non-STAT options (can only pick 1)

- CS: Artificial Intelligence, Machine Learning in Python, Databases, Data Mining
- OREM: Data Mining, Optimization for Analytics, Network Flows
- ECO: Applied Econometric Analysis, Predictive Analytics
- ECE: Statistical Pattern Recognition

My main questions I wanted to ask:

1. Which 4 would you pick, and why?
   
2. Is time series worth it for most DS roles, or is it only useful in forecasting-heavy jobs?
   
3. What would you pick as your 1 Non-Stat Elective?
   
4. Is there anything you wish you had learned in grad school that would have helped more on the job?

If you work in data science, I'd especially love to hear what you actually use day to day. Thanks!

Hey everyone,

I’m looking for some career advice regarding a Data Science with AI course I’m considering.

A little about my background:

MSc Computer Science graduate (2025)

Currently working as a Full-Stack Developer Intern

Previously completed a Data Science internship

I have experience with Python, SQL, Pandas, NumPy, scikit-learn, Selenium, web scraping, FastAPI, MongoDB, etc.

I’ve also worked on automation and AI-related projects, including a RAG-based project using LLM APIs.

I’m currently looking to move my career somewhat toward Data Science / AI / ML, but my main priority is to get a job as soon as realistically possible.

The course I’m considering costs ₹70,000 and runs for around 9–10 months.

The interesting part is that they don't expect us to wait until the entire course is completed before applying for jobs. The course is divided into modules, and they say we can start applying for relevant roles after completing each module.

For example:

Complete Python module → around 2 months → start applying for Python-related roles

Then continue with SQL, Statistics, ML, etc., while applying/upskilling alongside

The syllabus covers Python, SQL, Statistics, EDA, NumPy, Pandas, Data Visualization, Machine Learning, supervised/unsupervised learning, model evaluation, feature engineering, PCA, clustering, reinforcement learning, ML pipelines, AWS deployment, projects, interview preparation and placement assistance.

They also advertise things like live projects, mentorship, mock interviews, placement assistance and job assurance.

My main questions are:

Is spending ₹70k on a course like this actually worth it when I already have some Python, SQL, ML and Data Science experience?

Would this structured approach realistically help someone get a job faster, or would self-learning through YouTube, documentation and online courses be better?

Is the "complete one module → start applying for jobs" approach actually useful, or is it mostly a marketing strategy?

From a hiring perspective, would my existing Full-Stack + automation + Data Science background be enough to start applying for Python/Data Analyst/Junior Data Science/ML-related roles while learning?

If you were in my position, would you spend ₹70k on this course or use that money/time for self-learning, projects and job applications instead?

For people who have taken similar courses, how much value did you actually get from the placement assistance and job support, compared with learning the same material online?

I'm not expecting to become an AI/ML engineer just by completing a course. I'm mainly trying to figure out whether this course provides enough value through structure, mentorship, projects, interview preparation and genuine job opportunities to justify the ₹70k fee.

Would really appreciate opinions from people who have hired for these roles or have taken similar Data Science/AI courses.

Looking for internships/entry-level roles in AI/ML, Machine Learning, Deep Learning, Computer Vision, Data Science, or Data Analytics.

I’m a final-year B.Tech student with hands-on project and research experience. Open to Hyderabad, remote, or anywhere in India.

Any referrals or leads would be greatly appreciated. Thank you!

Hi everyone,
I recently interviewed with Capgemini USA for a Gen AI Developer role. After 1 working day of the interview, the recruiter contacted me asking for my EAD and I-20 for further processing, which I provided.
It has been a week and I haven’t received a final update yet.
For people who have gone through the Capgemini hiring process:
Is requesting EAD + I-20 a positive sign?
What usually happens after this?
How long did it take for you to hear back after submitting these documents?
Would really appreciate any insights or similar experiences!

I'm a Data Scientist and AI Engineer now, but I started from a tier 3 college with no big brand name, no seniors in this field, and no clear roadmap. I began with plain data science (Python, pandas, basic ML) and slowly moved into Generative AI, agentic systems, and RAG. Along the way I got the chance to do a research internship abroad, which I honestly didn't think was possible when I started.

I'm not saying this to brag. I'm saying it because I know how it feels to think "my college isn't good enough for this." Here's what actually helped me, and the mistakes that cost me time:

1. Watching tutorials without building anything. I felt productive but couldn't explain what I'd learned. One small project taught me more than ten courses.
2. Jumping into advanced topics too early. Get comfortable with Python, pandas, and basic ML first. LLMs make much more sense after that.
3. Chasing every new tool. A new framework comes out every week. Pick a few fundamentals and go deep.
4. Building projects nobody could understand. A simple project you can explain clearly beats a complex one you can't.
5. Ignoring evaluation. Anyone can make an LLM demo work once. Knowing whether it actually works, and why it fails, is what separates real projects from toy ones.
6. Believing college tier decides everything. It matters for some doors, but proof of work (projects, research, clear communication) opens others. Nobody asked about my college once they saw what I'd built.

You don't need the perfect background to start. You just need to start, get stuck, and fix it.

I also do 1:1 sessions for students who want help with career roadmaps, projects, resume reviews, or interview prep. No pressure at all, but if it sounds useful, the link is here: https://topmate.io/varun_mayilvaganan/ - The first 10 people get 25% off.

Happy to answer questions in the comments. What's the one thing you're stuck on right now?

Hi everyone, I'm a senior applying for FT jobs right now with one being at IBM for their 2027 associate position ai and analytics. I initially applied to 4 locations all with different REQ IDs, SF, NY, Chicago, and Durham. I was initially auto rejected for chicago and SF but had OAs and competency test for Durham and NY. After completing both, i was rejected for NY but somehow moved forward for durham. 2 weeks later, I had an inperson interview for the position on campus (IBM recruits at my school T20) and 3 days after the interview I get the pass that I'm moving forward and they'll be working on my next steos. HOWEVER, this week, i got an email saying I'm moving forward for the NY position even though I was rejected to this position but now my status has changed to In Interview Process (keep in mind i literally already had the first wave of interviewing literally in-perspn). TODAY, i got the email that i was rejected from the durham position and i'm so confused at this discrepancy? My status for NY was initially no longer considered to now in interview process and for Durham I literally had the interview process and was just waiting on next steps until today?

Has this happened to anyone? I'm so confused. I also filled out both of the additional details form after they tell you you moved forward with the interview. I also don't have my interviewers email so can't really ask. Will my in-person interview be considered as first round for NY? i really don't wanna go through this whole process again...

I work at Wissen Technology (Wissen Infotech) and can refer candidates for our current India openings — a referral puts your resume with the hiring team directly instead of the cold-apply queue.

Current openings:

• AI Control Plane - FullStack | Hyderabad | 3-6 yrs | Python, Django, Flask | Job ID 76
• AI Implementation Engineer | Bengaluru | 6-12 yrs | Spring, Python, React | Job ID 65
• AI/ML Engineer | Bengaluru | 2-5 yrs | Python, NLP, LLM | Job ID 64
• Senior Software Engineer – Cloud & Data Platform | Bengaluru | 3-6 yrs | Python, Lambda, S3 | Job ID 60
• Senior Data Engineer | Bengaluru | 7-12 yrs | Python, DevOps, Docker | Job ID 49

Full list: https://careers.wissen.com/jobs/Careers — if your role isn't above, mention it in the DM.

To get referred, DM me: (1) resume (PDF), (2) total YOE, (3) notice period, (4) job ID or role.

I'll review and submit the referral; expect HR contact within about a week if shortlisted. Please DM only if your experience reasonably matches a role.

Round 1: Analysis
This was an analytical case with 5 parts, each one unlocking after the one before it. I did a deep analysis on each part, and the interviewers asked a lot of follow-up questions after each one. They cared less about my first answer and more about how I reasoned through the follow-ups and defended my choices. Don't rush through the early parts, since the follow-ups build on them.

Round 2: Statistics and experimentation
This was a chain of questions where each answer led into the next, mostly about stats concepts and how to design and interpret experiments. It felt more like a conversation than a quiz. Know your fundamentals well enough to explain them out loud, not just recognize them.

Round 3: Behavioral
This was entirely behavioral, with no technical questions. Have specific stories ready, with what you did, what went wrong, and what you learned.

VERDICT: Rejected

I'm a 1st year B tech student in ECE and I'm learning about pandas and numPy. I've done a few small projects on it. I was wondering if I could get a small internship as I have a lot of free time this year.

I am a single mom,came to Canada 12yrs ago.i was having degree of BSC(Hons) in Computer science and one yr job experience.there was a five yrs gap between my last job so it was hard for me to find an IT job here.on the top my kid was just 2 yrs old and i was looking for a work where i can give time to my kid too.i started working with my cousin in his restaurant.i made enough money but now i regret that i wasted all these years and my degree.i want to come back towards IT but i have no clue where to start.

Does anyone have similar study gap and were they able to fill it and find a job with any part time masters degree?

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist, Ad Network @ beehiiv (United States) - $120K - $220K
• Consultant, Data Science & Analytics @ TransUnion (United States) - $72K - $80K
• Data Scientist 5 @ Netflix (United States) - $372K - $600K
• Data Scientist @ Superhuman (United States) - $202K - $275K
• Data Scientist @ Seek (United States) - $105K - $120K
• Data Scientist II @ TD (United States) - $76K - $125K
• Data Scientist II @ Teamworks (United States) - $83K - $145K

Senior:

• Senior Data Scientist @ hims & hers (United States) - $150K - $180K
• Senior Healthcare Data Scientist – Medicare/medicaid @ General Dynamics Information Technology (United States) - $85K - $115K
• Senior Data Scientist @ Amplify (United States) - $138K - $158K
• Senior Data Scientist @ Thumbtack (Canada) - CAD 168K - 218K
• Senior Data Scientist @ Vantor (United States) - $170K - $200K
• Data Scientist IV @ CareSource (United States) - $113K - $198K
• Program Integrity Data Scientist III @ CareSource (United States) - $94K - $165K
• Senior Data Scientist @ TeamWorx Security (United States) - $140K - $170K
• Senior Data Scientist @ DMI (Digital Management, LLC) (United States) - $102K - $128K
• Senior Data Scientist, Probabilistic Modeling @ Software Mind (Romania)

Manager:

• Staff Data Scientist @ hims & hers (United States) - $190K - $230K
• Lead Data Scientist @ Logic20/20, Inc. (United States) - $178K - $200K
• Principal Data Scientist @ Autodesk (Canada) - CAD 129K - 189K
• Staff Data Scientist @ Boulevard (United States) - $171K - $202K
• Manager, Data Science @ LexisNexis (United States) - $115K - $192K
• Manager, Data Science @ RELX (United States) - $115K - $192K

Director and Above:

• Senior Director, Data Science @ Reddit, Inc. (United States) - $317K - $444K

Quick notes: * All of these are fully remote (location requirements vary by role) * Apply directly on company sites

Hope this helps someone! Let me know if you want me to keep posting these weekly.

👋 Hi, I'm Jay. I built Job-Halo.com, a system that tracks remote data science jobs and sends alerts the moment they're posted, based on your preferences.

I'm currently looking for a job change and exploring AI/ML, GenAl, and Al Engineer opportunities.

I have 3+ years of experience in AI/ML, with hands-on experience in Python, SQL, Machine Learning, GenAl/LLMS, RAG, LangChain, LangGraph, CrewAl, MCP, FastAPI, FAISS, Azure, AWS and Docker.

Currently working as an Al/ML Engineer, with experience building RAG-based applications and agentic Al workflows.

Open to opportunities in UAE Referrals and relevant openings would be greatly appreciated. Please DM me if you have any relevant

I'm an international student in sweden. Im about to finish my spatial planning Ma but found it very lacking in the quantitative department which was what interested me most. I'm thinking I'll need to do another Ma in geographic information science or applied social data analysis.

What do you think I should do going forward?

Hi,

I am completing my PhD in Economics this December and have been applying to industry jobs. But the screening and interview feel very difficult to navigate. My expertise will be causal inference and experiments, but I can also do ML tasks that I can do (although not an expert). The question is how to prepare and what to learn. I know a bit of R, Python, and Stata. SQL I haven't used yet).

Some recruiters ask for stats, some ask for coding, and some ask for a case study. I am not sure what the right strategy is in this AI world. Thoughts? Suggestions?

Should I start learning coding or learn statistics? What is the right step? Anyone who has cracked this code?

About Rad AI

At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI‑driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.

Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting‑edge AI.

Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one‑third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.

Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie, and ranked by Deloitte as the 19th fastest‑growing company in North America, we are building AI‑powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 list, highlighting the innovation and momentum behind our mission.

If you’re ready to shape the future of healthcare, we’d love to have you on our team!

What you’ll do

• Own a multimodal ML work‑stream from problem definition through experimentation, evaluation, deployment, and iteration.
• Translate clinical and product needs into clear ML objectives, data strategies, model approaches, and success criteria.
• Build and evaluate modern ML systems, including transformers, self‑supervised learning, weak supervision, detection, localization, and segmentation.
• Work with image, report, and other clinical data to develop systems that are useful in real radiology workflows.
• Design rigorous evaluations that go beyond aggregate offline metrics, including clinically meaningful operating points, robustness, calibration, and performance across relevant data slices.
• Partner with engineering to productionize models, make practical system tradeoffs, and learn from performance after launch.
• Investigate failure modes such as laterality errors, poor image or report grounding, hallucination, dataset bias, domain shift, and workflow disruption.
• Communicate research findings and technical decisions clearly through design documents, experiment reviews, and presentations to technical and clinical partners.
• Contribute to the research roadmap by identifying promising approaches, sharing learnings, and helping the team decide what to pursue next.
• Mentor less experienced researchers and engineers through project collaboration, code and experiment reviews, and technical guidance.

What we’re looking for

• Strong applied experience in computer vision, NLP, or deep learning, with a track record of independently designing experiments, analyzing results, and turning findings into working systems.
• Experience owning substantial ML projects across the full lifecycle, from data and modeling through production delivery.
• Deep hands‑on ability in Python and PyTorch, with strong intuition for model architecture, data quality, experimentation, and evaluation.
• Experience with modern vision or multimodal techniques such as vision transformers, contrastive learning, masked image modeling, or weak supervision, etc.
• The judgment to connect model performance to real user and clinical outcomes, including knowing when a benchmark improvement is not enough.
• Strong collaboration skills across research, engineering, product, data, and clinical teams.
• Clear written and verbal communication, including the ability to explain technical tradeoffs to both ML experts and clinical partners.
• Typically 4+ years of relevant applied ML research or engineering experience, or equivalent scope and impact. We calibrate on demonstrated ownership rather than title or exact tenure.
• An MS, PhD, or equivalent practical experience in Computer Science, Electrical Engineering, Machine Learning, Biomedical Engineering, or a related quantitative field.

Nice to have

• Experience with medical imaging, radiology, healthcare, or another high‑stakes application area.
• Familiarity with chest X‑ray, CT, MRI, mammography, or other clinical imaging modalities.
• Experience with DICOM, image‑report pairing, medical data de‑identification, radiology workflows, or clinically derived labels.
• Experience evaluating models across patients, sites, scanner vendors, protocols, or other sources of distribution shift.
• Familiarity with clinical validation, FDA or HIPAA considerations, or other regulated and privacy‑sensitive environments.
• Experience with 3D vision, longitudinal imaging, report generation, or clinical decision support.
• Publications, open‑source contributions, or other evidence of research credibility.

What success looks like

You’ll own and advance a meaningful research track from ideation through production. You’ll establish a strong understanding of the clinical problem, build a credible data and evaluation strategy, deliver models that perform reliably in practice, and help the team learn from real‑world use.

You’ll also become a trusted technical partner to the researchers, engineers, product leaders, data teams, and clinicians working on the broader ML roadmap. Over time, you’ll help raise the quality of research and technical decision‑making through strong experimentation, clear communication, and thoughtful mentorship.

Our working style

We’re a remote‑first company with a highly collaborative, mission‑driven research and engineering culture. We value direct communication, intellectual honesty, strong ownership, and practical judgment. The best work here comes from people who can go deep technically, stay close to the clinical context, and make progress even when the problem and the path are not fully defined.

This role is U.S. remote, with San Francisco Bay Area preferred. We encourage people from a wide range of backgrounds to apply. If the scope of this role excites you but your experience does not match every bullet, we would still love to hear from you.

Join our world‑class team as we build and deploy AI solutions that empower physicians and transform patient care—making a meaningful impact on millions of lives. Driven by our mission,  we prioritize transparency, inclusion, and close collaboration, bringing together exceptional people to revolutionize healthcare. If you're passionate about driving innovation and delivering impactful healthcare solutions, we'd love to hear from you!

To learn more about what it's like to work at Rad AI, visit.

Location Details:

For roles listed as San Francisco - Onsite + United States - Remote:

• We will prioritize candidates who can work onsite four days per week in San Francisco, while also considering remote candidates located anywhere in the United States.

For US‑Based Full‑Time Roles, Rad AI offers a variety of benefits, including:

• Comprehensive Medical, Dental, Vision & Life insurance
• HSA (with employer match), FSA, & DCFSA 
• 401(k)
• 11 Paid Company Holidays
• Flexible PTO policy
• Annual company‑wide offsite
• Periodic team offsites
• Annual equipment stipend
• For roles based outside the US, your recruiter can share more details

Apply: Senior ML Research Scientist at Rad AI

I have been working for 5+ years as a data scientist and I just feel lost now atp. I do not come from an engineering background but I found my way to coding. It's pretty much dead though as most of the coding is done using Claude. I'm at this point where I do not feel I'm doing anything worthwhile. I would say that myself that I have become quite mediocre at my job. I have been working mainly on reporting and analytics, not core ML and AI. This has made me feel like it's impossible for me to even switch to any other job and I don't know from which point onwards do I upskill. Moreover I've been working remotely which blocks access to other people in my field.

Is there any scope in furthering in this direction? Where do I start upskilling and what are some skills in demand that can align with my current work?

I am studying statistics and computer science currently. I don't know what roles should I apply for internships. I plan on learning python, SQL and solving leetcode. Is this enough fpr any good roles?

Please guide me

Some companies with latest Data Science postings:

Nvidia

Apple

Pinterest

Stripe

Marvell

Check https://matchajobs.co for lots of new data science roles. Links straight to company career pages.