What you’ll do
Manage end-to-end data science projects from scoping to deployment, ensuring accuracy, reliability and measurable business impact
Translate business needs into actionable DS tasks, lead data wrangling, feature engineering, and model optimization
Communicate insights to non-technical stakeholders to guide decisions while mentoring a 14 member DS team.
Implement scalable MLOps, automated pipelines, and reusable frameworks to accelerate delivery and experimentation
What we’re looking for
4-5 years of hands-on experience in Data Science/ML with strong foundations in statistics, Linear Algebra, and optimization
Proficient in Python (NumbPy, pandas, scikit-learn, XGBoost) and experienced with at least one cloud platform (AWS, GCP or Azure)
Skilled in building data pipelines (Airflow, Spark) and deploying models using Docker, FastAPI, etc
Adept at communicating insights effectively to both technical and non-technical audiences
Bachelor’s from any field
You might have an edge over others if
Experience with LLMs or GenAI apps
Contributions to open-source or published research
Exposure to real-time analytics and industrial datasets
You should not apply with us if
You don’t want to work in agile environments
The unpredictability and super iterative nature of startups scare you
You hate working with people who are smarter than you
You don’t thrive in self-driven, “owner mindset” environments- nothing wrong- just not our type!