Job Description
<p>KEY RESPONSIBILITIES :<br/><br/>- Develop, validate, and deploy predictive, prescriptive, and scoring models to power product features and business decisions.<br/><br/>- Partner with the product management and data engineering teams to design and implement algorithms that directly impact customer experience and business growth.<br/><br/>- Drive feature engineering, model selection, and performance evaluation across diverse modeling use cases (scoring, forecasting, optimization, segmentation, simulation, NLP, etc.).<br/><br/>- Make analytical and technical decisions on modeling trade-offs (accuracy, interpretability, scalability).<br/><br/>- Ensure models are production-grade, explainable, monitored, and continuously improved as data and market conditions evolve.<br/><br/>- Stay up to date with emerging ML/AI techniques and proactively evaluate their applicability to business use cases.<br/><br/>REQUIRED SKILLS :<br/><br/>- Strong foundation in Machine Learning, Statistical Modeling, and Applied Mathematics, with proven experience in real-world problem-solving.<br/><br/>- Hands-on expertise in Python and R, including ML libraries (scikit-learn, XGBoost, PyTorch/TensorFlow for deep learning)<br/><br/>- Solid understanding of data preprocessing, feature engineering, and handling large-scale structured and unstructured datasets.<br/><br/>- Experience in building and deploying models such as : Scoring/response models, recommendation systems, forecasting, optimization, segmentation, causal inference.<br/><br/>- Excellent communication and stakeholder management skills, with the ability to effectively influence, align, and drive consensus across product, engineering, and business teams.<br/><br/>- Proven track record of leading analytics/modeling projects end-to-end.<br/><br/>DESIRED SKILLS :<br/><br/>- Exposure to Text Mining and NLP (topic modeling, sentiment analysis, embeddings)<br/><br/>- Knowledge of LLM-based applications is a plus.<br/><br/>- Knowledge of Bayesian analysis and probabilistic modeling.<br/><br/>- Experience with optimization algorithms, reinforcement learning, or simulation modeling.<br/><br/>- Working knowledge of cloud platforms (AWS) and ML pipelines is a plus.<br/><br/>- Exposure to Deep Learning<br/><br/>- Familiarity with digital marketing, SEO, and search-related modeling is a plus.<br/><br/>QUALIFICATIONS :<br/><br/>- Master's or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering).<br/><br/>- 4-6 years of experience in applied data science/modeling, ideally with projects spanning predictive modeling, NLP, optimization, and business-focused analytics.<br/><br/>- Experience delivering models into production environments (not just research/prototyping).</p> (ref:hirist.tech)