Data Scientist, Digital Ops

Who You'll Work With

You will become an integral part of our Operations Digital Assets team situated in the McKinsey Global Capabilities Center, Chennai. Our mission is to leverage technology to tech-enable Operations practice assets. You will play a pivotal role in developing data science assets including but not limited to GenAI features, data transformation pipelines across various Operations service lines.

Our team boasts a dynamic blend of data engineering and data science expertise focusing on key activities such as new solution development, and the enhancement of current solutions, all with a strong emphasis on data engineering and science. Collaboration with solution and service line leadership is a cornerstone of our efforts, ensuring we deliver maximum impact for our clients.

What You'll Do

You will help build/enhance solutions that leverage client data to deliver insights through enhanced analytical models including GenAI.

In this role, you will be responsible for providing GenAI/data science and automation expertise to our solutions including data extraction from different database systems, integrating, consolidating and cleansing data, cloud hosting new solutions and pipelines for building machine learning models, advanced prompting, building vector embeddings for semantic search, etc.,

You will build internal team assets/tools and deploy these tools in client infrastructure by collaborating with the product managers, asset leaders, developers and technical operations firm members. You will need to quickly learn new cutting-edge technologies and solve complex data processing problems. You will also help in bringing latest Generative AI tools/platforms into Operations practice.

Qualifications

• Bachelor’s degree in computer science, engineering, mathematics, or equivalent experience
• 2+ years of relevant experience with practical experience in Generative AI
• Extensive experience with at least one of the LLM providers (OpenAI, Anthropic, Cohere, etc.)
• Proven experience in generative artificial intelligence topics like vector embeddings, semantic search, RAG, advanced prompting, etc.
• Writing production-grade code in Python for machine learning in a professional setting
• Strong understanding of analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, kedro, mlflow)
• Familiarity with any cloud platforms (AWS, Azure, or GCP); hands-on experience is a plus
• Familiarity with containerization technologies (Docker, Docker-compose); hands-on experience is a plus
• Familiarity or hands-on experience with pipeline orchestration frameworks or orchestrators in general (Airflow, Argo Workflows, Kedro, Dagster, etc.)
• Familiarity or hands-on experience with testing libraries (e.g., pytest)
• Proven experience applying machine learning techniques to solve business problems; familiarity or hands-on experience with Python Dash or Django frameworks; hands-on experience with automation frameworks (CircleCI, Jenkins, Github Actions, Drone, etc.); exposure to the software development cycle
• Discernable communication skills, especially in breaking down complex structures into digestible and relevant points for a diverse set of clients and colleagues at all levels; high-value personal qualities including critical thinking and creative problem-solving skills, an ability to influence and work in teams, an entrepreneurial mindset, and ownership mentality
• A desire to learn and develop within a dynamic, self-led organization