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:
- D ata preprocessing, feature engineering, model training
- V alidation, offline evaluation, and sensitivity analysis
- M onitoring, 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.
- Solid 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 v ector e mbeddings and v ector d atabase s 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
- Masters or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering).
- 4 6 years of experience in applied data science/modeling, ideally with projects spanning predictive modeling, NLP, optimization, and business-focused analytics.
- Experience delivering models into production environments.

📌 Senior Data Scientist (Bengaluru)
🏢 Iquanti
📍 Bengaluru