SR. DATA SCIENTIST

Job Overview

We are looking for a Senior Data Scientist with a strong foundation in data science, machine learning, and AI who has a proven record of delivering high-visibility, high-ROI solutions to complex business problems. In this role, you will lead a small team of data scientists, set the technical direction, and own end-to-end delivery of impactful AI/ML solutions. You will spend most of your time enabling the team through delegation, coaching, and review, while stepping in hands-on for critical or highly complex work when needed.

This role suits someone who has been a standout individual contributor and is now ready to multiply their impact through people.

What your background should look like:

Experience

• 9–10 years of relevant experience in data science, machine learning, and AI, with a track record of owning and delivering end-to-end solutions.
• Demonstrated record as a strong individual contributor who delivered high-visibility, complex solutions with measurable ROI.
• Prior experience leading or mentoring a team (formal or informal), with exposure to people management and leadership responsibilities.

Education

• Master's or PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field (Bachelor's with exceptional experience may be considered).

Technical Skills

• Programming: Expert-level Python ; strong SQL; working knowledge of software engineering practices (Git, testing, code review, CI/CD).
• Machine Learning & Statistics: Deep understanding of supervised/unsupervised learning, time-series forecasting, experimental design, hypothesis testing, causal inference, and optimization.
• Deep Learning & AI: Hands-on experience with frameworks such as PyTorch, TensorFlow, or equivalent; experience with NLP, computer vision, or GenAI/LLMs (prompting, RAG, fine-tuning, evaluation) is strongly preferred.
• Data & Platforms: Experience with large-scale data processing (Spark or equivalent) and cloud platforms (AWS) and their ML services.
• MLOps: Experience deploying and monitoring models in production (e.g., MLflow, Docker, Kubernetes, model registries, pipelines).
• Visualization & Communication: Proficiency with tools such as Streamlit or equivalent; strong data storytelling skills.

Competencies