Data Scientist

Job Description -  Data Scientist 

 

Key Responsibilities: 

• Develop, validate, and deploy predictive, prescriptive, and scoring models to power product features and business decisions. 

• Conduct deep-dive analyses to extract meaningful insights from complex and large datasets; identify key drivers, patterns, and opportunities. 

• Partner with the product management and data engineering teams to design and implement algorithms that directly impact customer experience and business growth. 

• Own end-to-end model lifecycle management, including: 

• Data preprocessing, feature engineering, model training 

• Validation, offline evaluation, and sensitivity analysis 

• Monitoring, drift detection, and iterative improvements 

• Make analytical and technical decisions on modeling trade-offs (accuracy, interpretability, scalability) and ensure outputs are aligned with business objectives. 

• Ensure machine learning models are explainable, reproducible, and aligned with business objectives. 

• Present findings and recommendations to key stakeholders in a clear and actionable manner. 

• Drive experimentation through A/B testing and offline validation to evaluate model performance. 

• Stay up to date with emerging ML/AI techniques and proactively evaluate their applicability to business use cases. 

• Mentor and guide junior data scientists/analysts accelerating their technical growth and career development 

 

Required Skills: 

• Strong foundation in Machine Learning, Statistical Modeling, and Applied Mathematics, with proven experience in real-world problem-solving. 

• Strong software engineering skills with proficiency in Python and R, including ML libraries (scikit-learn, XGBoost, PyTorch/TensorFlow for deep learning) 

• Solid experience with data preprocessing, feature engineering, and working with large structured and unstructured datasets. 

• Experience in building and deploying models such as: Scoring/response models, recommendation systems, forecasting, optimization, segmentation, causal inference. 

• Strong collaboration skills with the ability to work closely with product, engineering, and business stakeholders. 

• Proven track record of owning analytics or modeling projects end-to-end. 

Desired Skills: 

• Knowledge of Bayesian analysis and probabilistic modeling. 

• Experience applying optimization or simulation techniques to real-world decision problems 

• Exposure to Text Mining and NLP (topic modeling, sentiment analysis, embeddings) 

• Experience working with large vector embeddings and vector databases is a plus. 

• Knowledge of LLM-based applications is a plus. 

• Working knowledge of cloud platforms (AWS) and ML pipelines is a plus. 

• Background in digital marketing analytics, including SEO, paid media or search-related modeling is a plus. 

Qualifications: 

• Master’s or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering). 

• Experience in applied data science/modeling, ideally with projects spanning predictive modeling, NLP, optimization, and business-focused analytics. 

• Experience delivering models into production environments.