[Remote] Data Scientist
Note: The job is a remote job and is open to candidates in USA. KANINI is seeking a pioneering, fully autonomous Senior Data Scientist to lead the complete data science lifecycle for their flagship prediction model project. The candidate will be responsible for sourcing, analyzing, and engineering all data to power predictive models, requiring deep AI/ML mastery and hands-on engineering skills.
Responsibilities
- Lead end-to-end analysis of large, complex, multi-source datasets to surface patterns driving model inputs
- Identify, collect, clean, validate, and transform all data required for prediction model consumption
- Design and maintain scalable, production-grade data pipelines (training, validation, inference)
- Perform deep EDA, data profiling, and quality audits to ensure model-ready data standards
- Architect, train, evaluate, and iterate ML models — supervised, unsupervised, and reinforcement learning
- Own feature engineering: selection, extraction, transformation, and dimensionality reduction
- Apply advanced techniques: deep learning, NLP, time-series forecasting, ensemble methods
- Benchmark, A/B test, and monitor models in production; drive continuous performance improvement
- Deploy models via REST APIs (FastAPI/Flask); ensure reproducibility and scalability
- Self-direct from problem definition through solution delivery with zero hand-holding
- Translate ambiguous business problems into precise, executable data science problem statements
- Communicate model results and data insights clearly to technical and non-technical stakeholders
- Document all experiments, methodologies, and outcomes — audit-ready and reproducible
- Champion best practices across the data science lifecycle; mentor junior team members
Skills
- B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or equivalent quantitative field
- 5+ years of hands-on data science experience with at least 2 years delivering production-grade ML models
- Proven ability to own and deliver end-to-end data science projects independently
- Portfolio demonstrating innovation in predictive modeling and measurable business impact
- Experience in a fast-paced, data-driven, decision-model environment
- Statistics (Bayesian inference, hypothesis testing, regression, distributions)
- Linear algebra, calculus, and probability applied to ML model design
- Supervised & unsupervised learning, anomaly detection, clustering
- Time-series analysis & forecasting: ARIMA, Prophet, LSTM
- Python (Expert): NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, Plotly
- SQL (Advanced): window functions, CTEs, query optimization
- Git / GitHub; CI/CD for ML; MLOps with MLflow or Kubeflow
- Docker & Kubernetes for model containerization and serving
- TensorFlow and/or PyTorch — deep learning architectures
- XGBoost, LightGBM, CatBoost — gradient boosting & ensemble methods
- Hugging Face Transformers — NLP, LLMs, and fine-tuning
- SHAP, LIME — model explainability and interpretability
- AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML
- Apache Spark / PySpark — distributed data processing
- Airflow / Prefect — pipeline orchestration
- Master's/Ph.D. strongly preferred
- LLMs / Generative AI / Prompt Engineering — strong advantage
- Snowflake Data Cloud: querying, Snowpark for Python ML pipelines
- Snowflake Cortex AI / ML Functions for in-database ML
- Dbt for data transformation; data governance within Snowflake
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