Data Scientist
Philips is hiring a Data Scientist in Bengaluru, India. Level rates it ; you can apply on Philips.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include OpenAI, Bedrock and PyTorch.
- Level lists 803 other open roles at Philips.
- The role was posted in the last few days.
AI in this role
Job Title
Data ScientistJob Description
Data Scientist
We're seeking with strong expertise in Agentic AI, Generative AI , Machine Learning, Predictive modelling, time-series analysis, longitudinal pattern mining to join our team. In this role, you will design and build innovative and ethically responsible AI solutions that effectively address real-world problems, balancing technical feasibility with user needs and business impact.
Your role:
- Design, train, and evaluate LLM-based solutions for various use cases, including retrieval-augmented generation (RAG), information retrieval & search, chatbots,
- structured information extraction and named entity recognition, text generation, summarization, and more
- Design, develop, and deploy a Machine Learning, Predictive modelling.
- Design, develop, and deploy agentic AI systems and autonomous AI workflows using large language models and advanced reasoning architectures
- Perform the time-series analysis, longitudinal pattern mining on the data sets.
- Perform the Research & Analysis (Example literature survey)
- Clean, preprocess, and curate data for evaluation, fine-tuning, and training
- Work closely with stakeholders across the organization to understand business needs, formulate problems, and deliver data-driven insights
- Keep up the latest advancements in generative AI research and integrate new techniques into the development process
You are fit if:
- You hold a master’s or Ph.D. in Computer Science, Data Science, or a related field
- You have at least five years hands-on experience developing AI/ML systems, with significant experience in LLMs, Generative AI, or AI agents in a professional or enterprise environment
- You are proficient in Python and relevant libraries for data science and deep learning (e.g., NumPy, Pandas, TensorFlow, PyTorch)
- You have a strong understanding of classic NLP and LLM-based approaches and hands-on experience with LLM-based architecture, such as RAG, prompt engineering, data synthesis, automatic evaluation, fine-tuning and agent frameworks
- Hands-on experience building and then iteratively refining model pipelines end-to-end, from data curation to evaluation
- Familiar with cloud computing platforms
- Experience with at least one GenAI platform (AWS Bedrock, Co-pilot, OpenAI, etc)
- Excellent communication skills and a collaborative mindset are essential
- Strong analytical and problem-solving skills, with attention to detail, round out your profile
Preferred Skills:
- Data Analysis & Interpretation
- Prompt Engineering
- Agentic AI
- Generative AI
- Predictive modelling
- Time-series analysis
- Longitudinal pattern mining.
- Data Governance
- Statistical Methods
- Statistical Programming Software
- Business Intelligence Tools
- Data Mining
- Machine Learning Engineering Fundamentals
- Machine Learning & Deep Learning
- Model Development & Deployment
- Research & Analysis
- Requirements Analysis
- Root Cause Analysis (RCA)
- Data Quality Management Systems
- Regulatory Compliance
How we work together:
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week.
Onsite roles require full-time presence in the company’s facilities.
Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
This role is an office-based role.
About Philips:
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.
• Learn more about our business.
• Discover our rich and exciting history.
• Learn more about our purpose.
If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.
#LI-PHILIN
How we rate this
Data Scientist at Philips rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI agents, Fine-tuning, and NLP. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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