SENIOR DATA SCIENTIST - Python

Title Data Scientist Skills (must have) 5+ years of hands-on experience in Data Science, Machine Learning, or Applied ML. Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. Strong Python programming skills with experience in: o pandas, NumPy, scikit-learn o TensorFlow or PyTorch for deep learning projects. Proven experience designing, training, tuning, and validating ML models: o Supervised (classification, regression) o Unsupervised (clustering, anomaly detection) o Time-series/forecasting o Strong expertise in feature engineering, EDA, and statistical analysis. Deep understanding of: o ML algorithms o Model evaluation techniques o Probability & statistics o Linear algebra & optimization fundamentals Experience working with large datasets using: o Apache Spark, Dask, Databricks o Or cloud ML platforms like Azure ML, AWS SageMaker, GCP Vertex AI Strong SQL skillswriting optimized, complex queries involving joins, aggregations, and window functions. Hands-on experience with MLOps concepts: o Experiment tracking (MLflow, Weights & Biases) o Model versioning & registries o CI/CD workflows for ML o Reproducibility and testing Experience deploying models in production using: o REST APIs o Docker containers o Serverless compute (Azure Functions, AWS Lambda, Cloud Run) Understanding of Responsible AI concepts: o Model monitoring o Fairness & bias evaluation o Drift detection o Explainability tools (SHAP, LIME) Strong data storytelling skills using visualizations: o Matplotlib, Seaborn, Plotly o Dashboard tools: Power BI, Tableau Skills (good to have) Experience with NLP: transformer models, embeddings, text classification, summarization. Exposure to LLMs, vector databases (Pinecone, Weaviate, Redis), and RAG architectures. Experience with Snowflake Snowpark ML, Databricks ML, or Azure ML pipelines. Exposure to feature stores (Feast, Databricks Feature Store, SageMaker FS). Container orchestration and microservices: Docker, Kubernetes. Experience with advanced methods: o Anomaly detection o Recommender systems o Causal inference or uplift modeling Experience with experimentation frameworks (A/B testing, CUPED, DoE). Key Responsibilities Collaborate with product owners, data engineers, software engineers, and subject-matter experts to identify and frame business problems suitable for ML or statistical modeling. Explore, clean, and transform raw data into high-quality datasets for modeling. Design, build, and validate machine learning models end-to-end, applying best practices in feature engineering, experiments, and evaluation. Build scalable training and inference pipelines in collaboration with data engineering teams. Deploy ML models into production, ensuring reliability, performance, and resilience. Conduct advanced statistical analysis and develop dashboards to generate insights for decision-makers. Monitor model performance, detect drift, diagnose data issues, and implement retraining or model refresh cycles. Apply MLOps best practices, including reproducibility, automated testing, model lifecycle management, and CI/CD integration. Stay current with the latest ML research, evaluate new techniques, and drive innovation in algorithms, architectures, and approaches. Mentor and guide junior data scientists through technical reviews and knowledge sharing. Document methodologies, assumptions, modeling processes, and results clearly for both technical and non-technical audiences. Soft Skills & Behavioral Expectations Strong analytical thinking and problem-solving skills. Ability to break down complex ML concepts for non-technical stakeholders. Ownership mindset takes initiative and drives projects independently. Strong collaboration skills across engineering, product, and business teams. Curiosity and commitment to continuous learning and experimentation. Ability to balance scientific rigor with practical business needs.