Job Title: Data Scientist
Location: Bangalore, India (Hybrid)
Experience: 5 to 10 years
Job Type: Full-Time
Job Description:
Supply Chain Intelligence is seeking an experienced Data Scientist to build customer-facing insights from idea to production. This is a hands-on, cross-functional role for someone who can move fluently across product discovery, data engineering, statistical analysis, machine learning, data modeling, and software delivery. You will work directly with product, engineering, domain experts, and customers to turn complex data into useful experiences: discovering insights, identifying statistically meaningful and mathematically exploitable patterns, validating them rigorously, and shipping them as reliable product capabilities.
Key Responsibilities:
• Own data-driven product capabilities end to end — from product hypothesis and data discovery through modeling, implementation, production deployment, measurement, and iteration.
• Explore complex data sets to find meaningful structure, latent relationships, anomalies, predictive signals, and mathematically exploitable patterns that can create customer value.
• Design and build data pipelines and analytical data models that make raw, messy, high-volume data usable for experimentation, machine learning, analytics, and customer-facing features.
• Develop statistical and machine-learning solutions including classification, ranking, forecasting, recommendation, anomaly detection, optimization, clustering, and other techniques appropriate to the problem.
• Translate models into product experiences by partnering with software engineers and product designers on APIs, services, workflows, visualizations, recommendations, and decision-support features.
• Define rigorous evaluation methods using offline metrics, experiments, benchmarks, error analysis, confidence estimates, and production telemetry to prove that a capability works and continues to work.
• Work directly with domain experts and customers to understand real decision-making problems, refine hypotheses, validate outputs, and distinguish statistically interesting findings from genuinely useful product insight.
• Productionize and operate data science systems with attention to latency, scalability, observability, reproducibility, cost, data quality, model drift, and graceful handling of uncertainty.
• Communicate clearly across disciplines explaining assumptions, evidence, trade-offs, limitations, and results to product, engineering, business, and technical stakeholders without unnecessary jargon.
Required Skills and Qualifications:
• 5 to 10 years of experience in data science, applied machine learning, quantitative product development, data engineering, or closely related work.
• Strong Python and SQL skills, with deep hands-on experience using Jupyter notebooks and modern data science libraries for exploration, experimentation, modeling, evaluation, and communicating results.
• Strong foundation in statistics and applied mathematics including probability, experimental design, hypothesis testing, regression, uncertainty, sampling, optimization, and model evaluation.
• Hands-on machine learning experience with common supervised and unsupervised methods and a strong understanding of feature engineering, leakage, overfitting, calibration, bias, and generalization.
• Strong working knowledge of databases and data platforms, including relational databases, SQL, analytical warehouses/lakehouses, data transformation, schema design, data quality, and working efficiently with large data sets.
• Strong data modeling skills across analytical and operational use cases, including relational, dimensional, document, and feature-oriented representations where appropriate.
• Experience delivering customer-facing products where models, algorithms, or data-derived insights are part of a real application, workflow, API, or interactive experience.
• Experience working with modern data and ML platforms such as Databricks, Spark,…