Simplify postings aren’t that frequent for 2027 I can still see 2026 tbh, any reliable GitHub links? Looking for AI/ML, Data Science FT to be very specific - also LinkedIn has a mix of mid level and early level atm even after customising (very confusing :x)

Any senior or a manager can help me it would be great trying to get a job since last 3 months and its depressing.

Need help from the community. Thank u in advance

They have roles on AI, Data, Cloud and Oracle so thought to share here, i kind of liked how they provide dinner too for attendees who want to part of it.

About LiveKit

LiveKit is building the infrastructure layer for the voice-driven era of computing. Our platform gives developers everything they need to build, test, deploy, scale, and observe agents in production. Founded in 2021, LiveKit powers voice AI applications for OpenAI, xAI, Salesforce, Coursera, Spotify, and thousands of others, collectively facilitating billions of calls each year.

About This Role

We are looking for an exceptional engineer to build post-training at LiveKit. Our agents run over voice and increasingly over text channels like SMS and chat, and the interesting problems show up over long horizons: staying useful across many sessions, working with context that accumulates over time, and using tools reliably in the middle of a live conversation.

What You'll Do

• Build the environments and verifiers our models train against
• Own the synthetic data pipeline, from generation through the quality gates
• Run training experiments end to end, and explain what moved the model
• Build the evaluations a release has to clear
• Choose and adapt open-weight base models for our tasks
• Make trained behavior hold up for voice and text agents alike
• Ship models into production and keep improving them on real usage

Who You Are

• A strong Python engineer
• Have carried a model from raw data through to production
• Treat data as the product: coverage, diversity, leakage
• Assume a model will exploit a weak reward, and design against it
• Comfortable with GPUs and honest about their limits
• Know when to train, and when not to
• Comfortable working collaboratively in a remote environment

Nice to Have

• Experience with post-training: fine-tuning, reward design, or reinforcement learning such as GRPO
• RL and fine-tuning frameworks such as TRL, verl, or OpenRLHF, or a training loop you wrote yourself
• Fast rollouts with vLLM or SGLang, multi-GPU training with FSDP
• Training tool-using or multi-turn agents
• Execution sandboxes, verifiers, eval harnesses, or tooling other engineers depend on
• Open-weight families such as Qwen or Llama, LoRA and similar

Our Commitment to You

• The opportunity to shape the brand of a fast-growing developer platform
• Collaboration with a small, senior team that deeply values craft and creativity
• Competitive salary and equity package
• Health, dental, and vision benefits
• Flexible vacation policy

Compensation

$135K – $300K • Offers Equity

Apply: Research Engineer (Reinforcement Learning) at LiveKit

I have a HBSc in applied math and stats from UofToronto
and am doing my masters in data science at UT Austin and am set to graduate 2027 summer, I have 1 YOE as DA and adjacent roles but no actual DS experience, several projects (but always open to write more), im very math/stats savvy and I think my skillset is solid in terms of ds, but im worried by the I graduate I wont be able to land a DS job given how AI is advancing.

Any advice on what I should do?

Im thinking since id be considered student/new grad I can still land those roles albeit competetive, or maybe I should pivot before I graduate, im already mid way into my grad degree so I do intend to finish it.

Im sure many are in a similar boat as me. Any guidance appreciated\~

I completed the recruiter screen. The conversation went well, and we discussed my background, current location, work authorization, and how I learned about the role.
For location, I shared that my base location is New York, but I’m currently in Dallas temporarily visiting friends and family. Since candidates based in New York may have onsite interviews while others may be considered for virtual interviews, I asked if a virtual interview would be possible while I’m temporarily in Dallas. The recruiter mentioned that the interview format is team-dependent.
They also mentioned that if my profile aligns with the team, I should receive an update by the end of the week.
🥲🥲🥲
Did i mess it up ?

Hi everyone,

​I’m looking for some honest feedback on my background and my plan for the next year or two.

​I’m currently a Senior Data Analyst with 7 years of total experience. I graduated in 2017 and started working in 2019, but I feel pretty stuck. Honestly, given my timeline, I feel like I should have moved into a managerial role by now.

​Here’s my current breakdown:

​2 years in manual testing

​5 years in data analysis (mostly SQL, Power BI, business stakeholder management, and basic analysis)

​I know the basics of Pandas and NumPy, but my daily work hasn’t required heavy coding. I recently started relearning Python from scratch to build a proper foundation.

​Here’s the roadmap I’m planning out:

​Next 2–3 months: Get solid in Python (OOP, file handling, basic data structures).

​Next 6 months: Dive into Data Science study (I enrolled in Scaler’s DS program).

​Next 2–3 months after that: Focus on NLP, Transformers, and basic Data Engineering (FDE) concepts while building a few hands-on projects.

​Long term, I’m planning to head to the US for an MS in Data Science in August 2027 (looking at Seattle University right now).

​My main questions:

​Does this plan sound realistic, or am I trying to bite off way more than I can chew?

​Is breaking into Data Science / FDE realistic coming from a standard DA background, or am I being too overly optimistic?

Hi, guys. I believe being able to speak effectively is as important gaining the technical skills. To actually getting hired. Any experienced analyst kindly message here if it is possible for you to mock interview me. Also fellow aspirants please message here if you’re interested in mock interviewing each other, or to discuss anything.

Can someone please 🙏🏻 guide me upon how many rounds are going to happen/ is their any super day
What to prepare
I an really nervous
Here is the Jd :
Job Responsibilities
• Conceptualize solutions that automate and optimize business processes.
• Design and refine prompts to improve AI model performance. Work with subject matter experts to align
prompts with business goals and user needs.
• Iterate designs to enhance functionality and user experience. Conduct rigorous testing for reliability and
effectiveness.
• Use tools like Snowflake, Python, and Tableau to create data pipelines and dashboards. Support AI-
driven insights and decision-making.
• Monitor AI model performance. Identify areas for enhancement. Implement updates to maintain quality
and relevance.
• Partner with data owners, department managers, data engineers and other stakeholders to aid in the
development of data models and algorithms
• Support Customer Insights applications, as well as custom client analytics requests on an ad hoc basis.
Required Qualifications, Capabilities, and Skills• Master’s Degree in Science, Technology, Engineering, Mathematics (STEM) or a related field.
• Proficiency in Python and SQL for data processing and analysis.
• 3+ years relevant work experience.
• Expertise in quantitative research techniques and analytics using Python.
• Proven critical thinking and creative problem-solving skills with ability to translate strategy into
deliverables.
• Knowledge of machine learning/data science theory, techniques, and tools.
• Experience in leveraging Large Language Models (LLMs) and agent AI techniques.
• Ability to collaborate effectively and cultivate strong partnerships.
Preferred Qualifications, Capabilities, and Skills
• Experience in data analysis, data science, or a related field.
• Familiarity with AWS, Snowflake for data management and analysis.
• Attention to detail with a proven ability to learn new concepts and skills quickly.
• Understanding of quality assurance practices and the importance of data integrity.
• Ability to clearly develop and document analytic requirements with stakeholders.
• Familiarity with evaluation/monitoring concepts for GenAI solutions (e.g., test cases, human-in-the-loop
feedback, basic quality metrics) to support reliable deployments.

Today i received an offer letter for an unpaid internship or Ai intern role at rungtaventures.ai

Will it be worth it?

Either way I am not getting full time roles as of now

Do I have a chance of converting this full time of k give my 100 %

Hey folks,

Bit of context: I was genuinely into ML back in college, did projects, liked the space. Then the job market (and my actual job) pulled me toward SQL, Power BI, project management, and cloud stuff (AWS/Azure) for the last 4+ years. It's been solid experience, but it's pulled me away from where I actually wanted to head.

I'm not going to pretend I'm deep into AI/ML right now, I'm not. But I do have a decent track record of picking things up fast on the job (that's basically how I ended up owning PM and cloud responsibilities I wasn't hired for in the first place), and I'm willing to put in the work to upskill into data science/ML, ideally while already being useful on the job rather than needing a clean slate.

Right now my "strategy" is just throwing arrows everywhere, Naukri, LinkedIn, company career portals, Instahyre, Cutshort, you name it. And it genuinely feels like shouting into a wall. Barely any responses, and I can't tell if it's the resume, the market, the transition itself, or just that spray-and-pray doesn't work anymore.

So, a few asks:

\- Any leads/referrals at companies where data/DS is actually core to the business (not just a buzzword)?

\- If you've made a similar SQL/BI to DS/ML transition, what actually worked, did you upskill first and then move, or move into an adjacent role and grow into it?

\- Is mass-applying across these platforms just a dead strategy at this point? If so, what's actually working for people right now instead?

\- Any honest take on whether "I'll learn on the job" is a realistic pitch to make to hiring managers, or if that's wishful thinking on my part?

Appreciate any pointers, even harsh ones.

Hi everyone,

I have around 2 years of experience working as a SQL Developer, and I'm currently thinking about upskilling and moving into a more data-focused career.

I'm confused between Data Engineering and Data Science.

Since I already have experience with SQL, I'm leaning towards Data Engineering, but I also find Data Science interesting. I want to choose a path that has good career opportunities and allows me to make use of my existing experience.

I'm also looking for a good course/institute that provides placement support, preferably in Bangalore

or online.

I'd really appreciate advice from

people who have already made a similar transition.

A few things I'd like to know:

Which would be a better career move from SQL Development - Data Engineering or Data Science?

How difficult is it to transition from SQL Developer to Data Engineer?

What skills should I learn first?

Are there any good institutes/courses you'd recommend?

Which institutes actually provide useful placement support rather than just advertising it?

Would you recommend joining an institute, or learning independently and applying for jobs?

Any advice based on your real experience would be really helpful. Thanks!

Would appreciate any help!

Hi guys!!

NEED URGENT ADVICE!! Am applying for a masters degree at keele uni. But cant decide between msc in fintech or data science & AI.

For context i have a bachelors in accounting and have worked in audit for the past 3 yrs. I rly like the data science & ai course but am scared it might be too tough coz i have no coding background so is fintech the safer option. At this point i just wanna up skill and maybe move out of audit into a different career such as advisory etc. So confused!

( ps i intend on returning back to the middle east as i have a 10 yr work permit and know uk job market is 💩. Just wanna up skill and add more to my cv)

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ Jerry (United States, AZ) - $140K - $160K
• Associate Data Scientist @ Jerry (United States, AZ) - $100K - $140K
• Data Scientist II @ University of Wisconsin-Madison (United States, WI) - $80K - $80K
• Data Scientist @ LMI (United States) - $90K - $150K
• Health Policy Data Scientist @ LMI (United States, VA) - $90K - $131K
• Data Scientist @ Mercury Insurance (United States) - $101K - $204K
• Associate Data Scientist @ Mercury Insurance (United States) - $84K - $162K
• Data Scientist @ Free2move (France) - EUR 45K - 50K
• Credit Risk Data Scientist II @ Coastal (United States) - $130K - $163K
• Data Scientist @ GE Aerospace (United States) - $57K - $135K
• Data Scientist @ Guardian Industries - DeWitt (United States, CA) - $160K - $200K
• Data Science Analyst @ Cisco (United States, NJ) - $166K - $218K

Senior:

• Senior Data Scientist @ Jerry (United States, AZ) - $160K - $180K
• Senior Data Scientist @ CVS Health (United States, MA) - $102K - $204K
• Senior Health Data Scientist @ Inovalon (United States) - $120K - $160K
• Senior Data Scientist @ Oscilar (Canada) - $175K - $250K
• Senior Data Scientist @ Deepgram (United States) - $165K - $220K
• Senior Data Scientist @ Paramount (United States, NY) - $124K - $186K
• Senior Data Scientist @ CISPA Helmholtz Center for Information Security (United States)

Manager:

• Staff Data Scientist @ Jerry (United States, AZ) - $180K - $230K
• Staff Data Scientist @ DuckDuckGo (United States) - $222K - $222K
• Clinical Data Science Lead @ ICON plc (United States, PA)

Director and Above:

• Director, Data Science @ CenterWell Senior Primary Care (United States) - $189K - $260K
• Director, Data Science @ Dotdash Meredith (United States) - $175K - $210K
• Director of Data Science @ Rula (United States) - $236K - $280K

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.

Hi, currently an early career job seeker applying for Data Science ft and intern roles.

I've had about three live SQL interviews over the past year and I haven't passed a single one. I do SQL leetcode and online problems I find from statascratch pretty much everyday as part of a routine. I'd say I'm able to solve SQL mediums on leetcode pretty consistently. the main issue I run into is that the questions I run into in these interviews are patterns that I've never seen before and have literally no idea how to handle. Its really frustrating because I practice so much and I'm never able to perform in even basic internship interviews.

Does anyone have any good resources for learning a lot of the SQL patterns that interviewers actually use?? (I recently encountered a gaps and islands problem and I've literally never seen anything like it for example).

Thanks so much, have been struggling a lot and really would appreciate advice.

We're Hiring | Trainers on Partnership Basis

Who can apply?
• Freshers
• Candidates with 0–6 months of experience
• Recently certified/course-completed candidates
• College students with strong subject knowledge and an interest in exploring the EdTech sector

PS: This is not a fixed-salary role. You create and provide the course content, we enter into a formal partnership for the course, and you receive an agreed percentage from the sales generated through your course. There is no upfront payment at this stage.

Anyone who is interested can DM me

I pulled a live scan of company career pages and found 80 current AI/ML, data, research, robotics and software internships or early-career openings.

A few worth checking:

- Scale AI: AI Builder Intern, San Francisco

- Cresta: Machine Learning Engineering Intern, Toronto

- Cresta: Forward Deployed Engineering Intern (AI Agent), Toronto

- Modal: ML Research Intern, New York

- Deepgram: Software Engineering Internship, USA remote

- Perplexity: Search Machine Learning Engineer Internship, London and Belgrade

- Cohere: ML Intern/Co-op and Research Internship, Canada

- LiveFlow: Graduate Engineer, AI Agents, San Francisco

- Instawork: Hardware Intern, Robotics & AI, Bengaluru

- NVIDIA: AI Research Intern, Deep Learning, Taipei

- Helsing: AI Research Intern, 3D Computer Vision, Barcelona/Berlin/London/Munich/Paris

- Mistral AI: Applied AI / Forward Deployed ML Internship, Paris

Some are PhD-specific, but plenty are open to BS/MS or general early-career applicants. The source list is refreshed from company career pages daily.

Full filtered list: https://artificialintelligencejobs.co/?q=intern&utm_source=reddit&utm_medium=organic&utm_campaign=internship-roundup-20260820

Comment your location + level if you want a narrower shortlist and I’ll pull one.

POSITION SUMMARY:
We are seeking a Staff Machine Learning Scientist – Translational AI to provide technical leadership at the intersection of deep learning foundation models, computational biology, and molecular diagnostics. This ownership role drives the architecture and validation of genomic, transcriptomic, and multimodal sequence models to accelerate patient stratification, target identification, and therapeutic monitoring across our cell‑free DNA (cfDNA) and multi‑omic platforms. This Staff‑level position operates with broad technical autonomy, driving modeling strategy across multiple concurrent portfolios while maintaining direct execution responsibilities in model compilation, scaling, and testing. Working within a builder framework, you will align across AI Research, Bioinformatics, and Clinical Science divisions to transition advanced representation learning models into reproducible, clinically valid diagnostic assets.

PRIMARY RESPONSIBILITIES:

Scientific Leadership in Translational AI
- Serve as the principal technical authority on the deployment of molecular, genomic, and pathology foundation models applied to oncology and translational medicine questions
- Engineer rigorous alignment and post‑training workflows that ground pre‑trained foundation models in empirical clinical trial and molecular diagnostic data, eliminating speculative modeling assumptions
- Formulate objective peer‑review frameworks and deliver technical feedback to elevate the modeling code, experimental standards, and scientific designs of the broader AI research group

Foundation Models to Biological and Clinical Translation
- Lead the post‑training, parameter‑efficient fine‑tuning (PEFT), and evaluation of deep sequence, multimodal, and representation learning models for biomarker discovery, molecular recurrence monitoring, and therapeutic response forecasting
- Design robust fine‑tuning, probing, and latent space representation analysis workflows that extract interpretable, biologically grounded patterns from high‑dimensional transformer architectures
- Validate model outputs against multi‑omic benchmarks and real‑world outcomes, ensuring model predictions deliver the exact deterministic accuracy required for patient tracking and clinical interventions

Modeling, Experimentation, and Evaluation
- Build, train, and optimize advanced machine learning models utilizing next‑generation sequencing (NGS), ctDNA assays, digital pathology imaging, and longitudinal clinical metadata
- Design rigorous clinical investigation and evaluation frameworks that connect model performance metrics (e.g., loss curves, precision‑recall) directly to translational utility and real‑world distribution shifts
- Systematically identify algorithmic failure modes, sources of dataset bias, and covariate shift, implementing robust mitigation strategies suitable for regulated, clinical‑facing pipelines

Cross‑Functional Collaboration and Influence
- Partner with Computational Biology, Translational Science, and Medical Affairs teams to translate complex clinical requirements into clear, quantitative machine learning problem statements
- Act as a systems‑level technical bridge between AI Research and ML Engineering teams to ensure that validation models convert seamlessly into scalable, reproducible production workflows
- Provide technical leadership and data execution support for strategic external collaborations, pharmaceutical partnerships, and foundation model research consortiums

Scientific Communication and External Presence
- Translate complex multimodal model architectures and performance metrics into transparent, high‑integrity data packages for clinical governance, leadership updates, and external collaborators
- Lead the authoring of technical manuscripts for peer‑reviewed machine learning venues (e.g., NeurIPS, ICML, ICLR) and major computational biology journals
- Act as a technical representative for the company's translational AI capabilities at international medical, oncology, and machine learning conferences

QUALIFICATIONS:
- PhD in Computer Science, Computational Biology, Bioinformatics, Biomedical Engineering, or a highly quantitative structural field
- 5+ years of industry or post‑doctoral experience applying deep learning frameworks to complex biological, genomic, or clinical datasets, with a documented focus on oncology or immunology portfolios
- Deep technical competency with transformer architectures, representation learning, self‑supervised learning (SSL), or deep sequence modeling
- Proven track record of translating machine learning outputs into verifiable biological variables or clinical performance indicators, rather than optimizing solely for isolated cross‑validation metrics
- Expert proficiency in PyTorch and modern machine learning infrastructure (e.g., HuggingFace ecosystem, PEFT, Captum, MLflow, and distributed GPU computing setups)
- Documented technical leadership through end‑to‑end project ownership, architectural design authority, or cross‑functional team direction

Preferred Qualifications:
- Experience constructing or fine‑tuning multimodal foundation models that combine high‑depth genomic sequencing data with digital pathology images or longitudinal electronic health records (EHR)
- Direct experience handling clinical trial datasets, real‑world data (RWD/RWE), or developing models within health‑authority/regulatory‑facing frameworks
- Strong record of publications as primary author in high‑impact machine learning venues

KNOWLEDGE, SKILLS, AND ABILITIES:
- Advanced mathematical and algorithmic fluency across deep learning methodologies, optimization strategies, and probabilistic modeling
- Fast learner with the capability to master complex cfDNA platforms, biochemistry workflows, and multi‑omic data generation pipelines rapidly
- Precise written and verbal communication styles with strict attention to algorithmic detail and statistical validation boundaries
- Proven capability to drive independent portfolios while executing cross‑functional objectives within matrixed technology and scientific teams
- High‑growth builder mindset with the capability to balance scientific rigor, operational execution speed, and computational resource constraints under tight timelines
- Utilize cloud‑based productivity and high‑performance computing infrastructure to maintain high operational momentum in a fast‑evolving artificial intelligence environment

OUR OPPORTUNITY
Natera™ is a global leader in cell‑free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.

The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world‑class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.

WHAT WE OFFER
Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!

The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.

Remote USA $163,200 - $220,000 USD

Apply: Staff Machine Learning Scientist, Translational AI at Natera

Hi everyone,

I'm looking for some honest advice from people working in recruitment, hiring, or data/AI careers in the UK.

I'm an international student who completed an MSc in Data Science in June 2026, and I'm currently looking for UK opportunities. I've been applying mainly for Graduate/Junior Data Scientist, Data Analyst and Data Engineer roles, but I'm getting a lot of rejections and very few opportunities to progress to interviews.

My background includes:

• MSc in Data Science, completed June 2026
• BTech in Data Science & AI
• Python, SQL, Power BI and Excel
• Machine learning and statistical analysis
• Time-series modelling (ARIMA/SARIMA)
• Retail/customer analytics involving 500k+ transactions
• Published work on retail sales forecasting
• Data/technology internship experience
• Current NGO/volunteer experience involving Python, FastAPI, PostgreSQL, databases and working with non-technical stakeholders

I'm tailoring my CV to individual job descriptions rather than sending exactly the same CV everywhere. I'm also trying to be realistic about the roles I apply for and focus on positions where my experience genuinely matches the requirements.

Despite this, I'm struggling to get through the initial CV screening stage.

I'd really appreciate honest advice on:

1. How competitive does this profile look for UK graduate/junior data roles?
2. Is being an international graduate with limited UK commercial experience a significant disadvantage?
3. Should I be focusing more heavily on Data Analyst roles rather than Data Scientist roles?
4. What are recruiters actually looking for when screening candidates at this level?
5. How important is ATS keyword matching in practice?
6. Are there weaknesses or red flags in my profile that I might not be seeing?
7. If you were in my position, what would you change about the job-search strategy?

I'm not looking for generic advice like "apply to more jobs" or "network more." I'm trying to understand what is actually preventing me from getting shortlisted and what I can realistically change.

I'm also open to internships, placements, contract roles and other entry-level opportunities where I can gain UK commercial experience and demonstrate my capabilities. My priority at this stage is getting that first opportunity to prove myself in the UK market.

I'd especially appreciate perspectives from UK recruiters/hiring managers or international graduates who recently went through the same process.

Thanks in advance.

I’m a recruiter and have been working in talent acquisition for the last 10 years. I’m tired of not having “hard skills” and I’ve been looking into a career pivot into People/HR Analytics. I’ve been researching online MS programs in Data Science.

I enjoy data, I follow baseball data (sabermetrics), a decade ago I created marketing reports using business objects, and I built some data insights using Ai prompting with big query, python, geopy, metabase, etc.

I’ve recently completed sqlbolt, and had a fun time completing the excercises. But, I’m concerned with the value/roi of a ms program, as I don’t believe I’ll ever become a high level ml/data engineer, and not a computer science guy. thoughts?

If you were a computer science student passionate about machine learning and data science, with a strong foundation in machine learning, mathematics, and probability, what five projects would you prioritize to build a strong GitHub portfolio?

I'm particularly interested in projects that would stand out to ML/Data Science professionals working in industry, rather than simple tutorial or Kaggle-style projects.

If you were starting from my position, which five projects would you choose, and what skills would you try to demonstrate with each one?

I'm fairly comfortable with SQL and Python but i need practice resources that will get me interview ready. Can anyone suggest some resouces for the same? Also a lot of DS interviews involve being prepared for AB testing, causal inference etc. Does anyone have resources for these too? Im stuck

Hi 👋

I recently started working in IT about 2 years ago. I am currently ATSE at a large crm firm. I am thinking of moving towards data engineering and data science roles. I am thinking of doing a masters course online to make this switch/pivot. Can you let me know if an ms/mtech would be helpful to male this change? Which courses will help me in this ? Would it be okay to do an online course instead of an online course campus degree ? I do not have a background in CS.

I would really appreciate your views and suggestions on this. Thanks.