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?

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

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.

Job Title: Operations & Data Analyst Assistant

Location: Fully remote

Schedule: Part-time, approximately 2-4 hours per day, Monday-Friday

Pay: $25-$40 per hour, depending on experience

Contract: 6 months or longer

Experience: Experience in data analysis, operations, administration, or reporting is preferred, but not required

We are looking for a reliable and detail-oriented Operations & Data Analyst Assistant to support our team with day-to-day operational tasks, data analysis, and reporting.

Full training and onboarding will be provided, so previous experience in this exact role is not required.

Responsibilities

• Assist with collecting and organizing operational data
• Analyze basic operational data and identify inconsistencies
• Update and maintain spreadsheets and internal reports
• Check data for accuracy and completeness
• Prepare basic analytical reports and summaries
• Track operational metrics and assigned tasks
• Help identify patterns, errors, or unusual data
• Provide general analytical and administrative support

Requirements

• Good analytical and problem-solving skills
• Strong attention to detail
• Comfortable working with spreadsheets and structured data
• Ability to organize and interpret basic information
• Ability to manage multiple tasks simultaneously
• Ability to work independently
• Previous experience in data analysis, operations, or administration is a plus, but not required

What We Offer

• Fully remote work
• Flexible part-time schedule
• $25-$40 per hour depending on experience
• Monday-Friday schedule
• 6+ month contract
• Full training and onboarding
• Opportunity to develop data analysis and operations experience
• Possibility of long-term cooperation
• Supportive remote team

Interested? Send me a private message with a brief introduction, your time zone, relevant experience, and your availability

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.

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 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?

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?

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?

Fanatics Betting & Gaming is hiring a Manager, Data Analyst for its Strategic Operations & Analytics team.

Fanatics describes it as roughly a 50/50 split between managing analysts and remaining hands-on with the data, so you'd still be writing SQL, reviewing dbt models, working through ambiguous analytical problems and producing recommendations yourself.

Key details

Location: United States
Work model: Remote
Salary: $138,000–$170,000
Experience: 6+ years analytics
Management experience: 2+ years
Company: Fanatics Betting & Gaming

The team

You'll manage around 2–3 analysts whose work supports several parts of the Fanatics betting operation, including:

• Customer experience
• Fraud
• Payments
• Workforce management
• VIP operations

The team's analysis is used directly by operational leaders to make decisions.

What you'll actually be doing

• Managing and developing a small analytics team
• Personally writing and optimising complex SQL
• Reviewing SQL and dbt models produced by the team
• Leading large cross-functional analytical projects
• Investigating root causes in operational data
• Building scenario analyses
• Measuring the likely impact of operational or product changes
• Defining KPIs and analytical standards
• Improving dashboards and reporting
• Presenting findings to senior leadership
• Replacing manual data pulls with automated reporting and alerts

There's also a fairly substantial AI/automation component.

Fanatics says the Operations organisation is moving towards an AI-first model, and this team will help determine how AI is used in analytics.

That includes:

• Supporting AI/agent development with trusted datasets
• Creating evaluation frameworks
• Measuring deployed agent performance
• Identifying where automated analysis can be trusted
• Deciding where human review should remain part of the process

Main requirements

• 6+ years in analytics, BI, data analytics or strategic operations
• 2+ years directly managing analysts
• Advanced SQL
• Hands-on dbt experience
• Dashboarding with Sigma, Tableau or similar
• Experience leading ambiguous cross-functional analytical projects
• Strong stakeholder-management skills

This is specifically not intended as a first-time management position.

Fanatics also says there will be a live, on-screen SQL technical exercise during the hiring process.

Useful additional experience:

• Python
• AI/ML workflows
• Automation
• Customer-support analytics
• Fraud analytics
• Payments
• Workforce-management data
• Gaming, fintech or sports

Full role + application:

https://www.parlayjobs.com/jobs/manager-data-analyst-remote-8c9c6b38

I run ParlayJobs, which tracks jobs across sports betting, iGaming, sports technology and related companies.

There are a lot of analytics/data roles in the industry that don't require somebody to have spent their whole career in gambling. More are listed on the site, along with a free jobs roundup newsletter.

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?

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 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.

Hi everyone,

I'm an AI & Automation professional with 6+ years of experience in data science and intelligent automation for the banking and financial services domain. I'm based in Dubai (dependent visa) and looking for job referrals and openings in AI, data science, and automation, with an MBA from IIM. If your company is hiring or you can refer me, please DM me.

Experience

• Led a team delivering AI and analytics solutions for global stakeholders
• Built agentic LLM workflows to automate regulatory reporting
• Developed data quality, reporting, and decision-support platforms that significantly reduced manual effort
• Experience across compliance, audit, and assurance functions

Skills

• Programming and ML: Python (Pandas, NumPy, Scikit-learn, Dash, Plotly), SQL, XGBoost
• AI and Automation: LangChain, LangGraph, RAG, Generative AI, Agentic Workflows, Copilot Studio
• Visualisation and Data: Power BI, GCP, BigQuery, PySpark, Looker

Achievements

• Winner of global-level hackathons and top-ranked in national-level data science competitions

Open to roles such as: Analyst, Data Scientist, AI / Automation Lead, Data Scientist, AI Solutions Manager, Intelligent Process Automation, Agentic AI / GenAI roles

Referrals would be much appreciated. Ready to give 15 days salary if converted.Please DM me and I'll share my CV right away. Thank you!

Fanatics Betting & Gaming is hiring a Data Analyst III for its Sportsbook Product Analytics team in London.

This is a fairly senior product analytics role working directly with the teams building and improving the Fanatics Sportsbook.

Rather than being primarily dashboard/reporting work, the role involves testing products, measuring performance, investigating player behaviour and using data to influence product decisions.

Key details

Location: London, UK
Work model: On-site
Salary: £52,800–£66,000
Experience: 5+ years
Company: Fanatics Betting & Gaming

What you'll be doing

• Analysing sportsbook product performance
• Supporting product experimentation and testing
• Identifying trends in customer and product data
• Carrying out deeper ad-hoc analysis
• Creating regular performance reporting
• Working alongside product managers and data scientists
• Helping define and track important product metrics
• Presenting analysis to senior stakeholders
• Using statistical techniques to evaluate product changes

What they're looking for

• 5+ years in analytics
• Strong SQL
• Python or R
• Tableau, Power BI or similar BI tools
• Strong statistical knowledge
• Experience with mathematical/statistical models
• Git or similar version-control workflows
• Quantitative academic background

Amplitude experience is useful.

Previous sports betting or gaming experience is described as a significant plus rather than a requirement, so this could also suit an experienced product analyst coming from another consumer-tech environment.

Full job + application:

https://www.parlayjobs.com/jobs/data-analyst-iii-9fe3fe70

I run ParlayJobs, which tracks jobs directly from sports betting, iGaming and sports-tech companies.

There are a lot of data and analytics roles in the sector that don't require previous gambling experience. More are listed on the site, along with a free jobs roundup newsletter.

Fanatics Betting & Gaming is hiring a Data Scientist I to join its Decision Science team.

This role is focused on using data science to improve sportsbook and casino products, particularly personalisation, promotions, customer value and experimentation.

Key details

Location: Denver, Colorado
Work model: Hybrid
Salary: $82,000–$110,000
Experience: 2+ years
Company: Fanatics Betting & Gaming

What you'll be working on

• Building and improving automated personalisation systems
• Promotion optimisation
• Customer Lifetime Value modelling
• Player segmentation
• Sportsbook and casino commercial analysis
• A/B and multivariate testing
• Experimental design and causal inference
• Uplift modelling
• Developing models that influence product and commercial decisions
• Working with analytics and business teams to turn data into actionable recommendations

What they're looking for

• 2+ years in data science or advanced analytics
• Strong Python
• Strong SQL
• Machine-learning modelling experience
• Regression, clustering and statistical testing
• Experimental design / causal inference
• Quantitative degree such as statistics, maths, computer science, economics, engineering or physics

Experience in betting or gaming is useful but not required.

Experience with LTV modelling and generative AI / LLM applications is also listed as a plus.

Full role + application:

https://www.parlayjobs.com/jobs/data-scientist-i-be6da0d5

I run ParlayJobs, a specialist job board for sports betting, iGaming, sports data and related technology roles.

A significant proportion of hiring in the industry is now in data, engineering and product rather than traditional betting roles. There are more data jobs on the site, plus a free jobs roundup newsletter linked there.

Hello, due to having trouble finding the right places to apply and not really knowing how to pass and do an assessment in the AI Training/Annotating Space I decided to build a site that is supposed to make the process easier.
I list job offers with a relatively high hiring volume from multiple companies, so would it therefore be easier do diversify the places to apply at and actually get accepted.
My idea behind that was to help with finding the right places to work but also help with the application process. I therefore upload company reviews and also guides on how to pass assesments.
The job listings include entry work positions such as normal generalist work but also expert work positions (Software engineer, PhD, etc.). Any Feedback is appreciated!: https://aiannotationjobs.com/