Master’s degree in quantitative fields such as Mathematics, Computer Science, Engineering, Physics, or a related discipline
2+ years of professional experience in data science, data engineering, or a closely related field
Experience with data engineering practices including ETL pipelines, orchestration tools such as Airflow, and big data platforms like Databricks; hands-on experience with data modelling techniques (e.g., 3NF, data vault, etc.), ability to work with both structured and unstructured data
Strong applied data science skills with experience in supervised and unsupervised learning, time series forecasting, clustering, optimization, geospatial modeling, and generative AI; familiarity with libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, Hugging Face Transformers, and pandas
Solid engineering capabilities in Python and JavaScript, with hands-on experience building and operationalizing solutions using FastAPI and React
Comfortable working with Git, CI/CD tools, and modern development workflows
Familiarity with Azure cloud services; experience with Kubernetes, Docker, and distributed computing frameworks is a plus
Highly inquisitive and creative problem-solver with a passion for turning data into actionable insight
Entrepreneurial and self-starting mindset, comfortable navigating ambiguity in a fast-paced, dynamic environment
Collaborative, professional, and team-oriented, with a strong sense of ownership and service
Strong communication skills with the ability to convey complex technical topics to both technical and non-technical audiences