Description

About Dsights Dsights is an analytics-focused organization delivering advanced data science and machine learning solutions for business decision-making. The company specializes in structured data analytics across multiple industries, enabling organizations to unlock value through predictive and statistical modeling. Job Summary Dsights is seeking a skilled Data Scientist to develop and deploy machine learning models on structured data across retail, FMCG, and fintech domains. The role involves end-to-end analytics ownership—from data preparation to model deployment—working closely with business stakeholders to deliver actionable, decision-ready insights. Key Responsibilities Develop, validate, and deploy machine learning models for use cases such as churn prediction, cross-sell, credit risk, pricing, and customer analytics Design and manage end-to-end data pipelines, including data cleaning, feature engineering, model training, evaluation, and monitoring Analyze large-scale transactional and customer datasets to generate insights for marketing, sales, supply chain, and risk teams Translate business problems into analytical and statistical models in collaboration with domain experts Present findings clearly to stakeholders with a strong focus on business impact Maintain reproducible, well-documented code using Python, SQL, and Git Contribute to model governance, experimentation documentation, and analytics best practices Required Qualifications PhD (submitted or final stage) in Statistics, Mathematics, Computer Science, Econometrics, Operations Research, or related quantitative fields OR 1–3 years of hands-on industry experience in machine learning on structured data Technical Skills Strong foundation in machine learning algorithms for structured data: Linear & Logistic Regression Tree-based models Gradient Boosting Regularization techniques Model diagnostics and validation Proficiency in Python (pandas, scikit-learn) Strong working knowledge of SQL Experience handling large, complex, real-world datasets Preferred Experience Exposure or strong interest in Retail, FMCG, Fintech, Credit Risk, or Customer Analytics Experience transitioning analytical insights into business decisions Familiarity with industry ML workflows and reproducible research practices Key Competencies Strong analytical and statistical thinking Clear and confident communication skills Curiosity, ownership mindset, and problem-solving attitude Ability to collaborate effectively with cross-functional teams Education Postgraduate: M.Sc / M.A in Mathematics, Statistics, Economics, or related fields Doctorate (Preferred): PhD in Statistics, Economics, Mathematics, or allied disciplines

Role and Responsibilities

• Key Responsibilities Develop, validate, and deploy machine learning models for use cases such as churn prediction, cross-sell, credit risk, pricing, and customer analytics Design and manage end-to-end data pipelines, including data cleaning, feature engineering, model training, evaluation, and monitoring Analyze large-scale transactional and customer datasets to generate insights for marketing, sales, supply chain, and risk teams Translate business problems into analytical and statistical models in collaboration with domain experts Present findings clearly to stakeholders with a strong focus on business impact Maintain reproducible, well-documented code using Python, SQL, and Git Contribute to model governance, experimentation documentation, and analytics best practices