DATA SCIENTIST

AI & Machine Learning

• Design, develop, and deploy predictive and prescriptive analytics solutions.
• Apply machine learning techniques including regression, classification, clustering, anomaly detection, forecasting, and optimization.
• Conduct exploratory data analysis, feature engineering, and model evaluation.
• Build scalable ML pipelines and monitor model performance.

Generative AI

• Develop GenAI solutions using LLMs and foundation models.
• Build RAG (Retrieval Augmented Generation) applications using vector databases and embeddings.
• Utilize frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar agentic AI frameworks.
• Evaluate and optimize prompts, retrieval strategies, and model performance.

Data Engineering & Platform

• Work with structured and unstructured data from enterprise systems.
• Build data pipelines using SQL, Azure Data Factory, Databricks, or similar technologies.
• Develop and deploy solutions on Azure, AWS, or cloud-native platforms.
• Collaborate with engineering teams to productionize AI solutions.

Business Partnership

• Translate business challenges into analytical frameworks and AI solutions.
• Present insights and recommendations to stakeholders and leadership.
• Drive adoption of AI solutions and measure business impact.

Required Skills

Technical Skills

• Strong Python programming skills.
• Expertise in Pandas, NumPy, Scikit-Learn.
• Hands-on experience with TensorFlow, PyTorch, or similar frameworks.
• Strong SQL skills.
• Experience with Azure AI Foundry, Azure ML, Databricks, or AWS SageMaker.
• Experience with APIs, model deployment, Docker, and CI/CD pipelines.
• Understanding of MLOps and model lifecycle management.

GenAI Skills

• Hands-on experience with LLMs (GPT, Claude, Llama, Mistral, etc.).
• RAG architecture, embeddings, and vector databases.
• Prompt engineering and AI evaluation frameworks.
• Experience building AI agents and workflow automation solutions.

Analytics & Statistics

• Statistical modeling and hypothesis testing.
• Forecasting and time-series analysis.
• Experimental design and A/B testing.
• Model evaluation and explainability techniques.

Preferred Experience

• Manufacturing, Supply Chain, Automotive, Industrial, or Enterprise Analytics domain experience.
• Experience delivering AI solutions from ideation through production.
• Familiarity with Power BI, Tableau, or other visualization platforms.
• Experience working in global and cross-functional teams.

Values: Integrity, Accountability, Inclusion, Innovation, Teamwork