Data Scientist

Job description

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, Snowflake, or equivalent tooling, and with cloud environments such as AWS or Azure. • Understanding of the full ML lifecycle, including reproducible experimentation, feature development, experiment tracking, model/version management, evaluation, batch and online inference, monitoring, drift detection, and retraining strategies. • Ability to frame ambiguous problems quantitatively, define measurable outcomes, choose an appropriate analytical approach, and know when a simpler statistical or deterministic method is better than ML. • Excellent communication and cross-functional collaboration skills with the ability to work effectively with product managers, software engineers, data engineers, designers, domain experts, and business stakeholders. Preferred Qualifications: • Proven track record of delivering machine-learning solutions into production and demonstrating measurable customer or business impact. • Breadth across multiple ML problem families, such as classification and regression, ranking and recommendation, clustering, anomaly detection, forecasting and time-series modeling, optimization, and probabilistic or statistical inference. • Experience working across data modalities including structured/tabular data, text and NLP, images/computer vision, embeddings, and multimodal systems that combine text, image, and structured data. • Hands-on experience with modern ML and deep-learning ecosystems such as scikit-learn, PyTorch, TensorFlow, XGBoost/LightGBM, Hugging Face, or equivalent frameworks. • Experience with experimentation and ML tooling such as Jupyter, MLflow or equivalent experiment tracking, model registries, feature engineering workflows, and reproducible evaluation pipelines. • Experience designing and evaluating predictive, generative, or retrieval-augmented systems, including LLMs, embeddings, retrieval, model evaluation, and confidence/uncertainty-aware outputs where appropriate. • Strong visualization and exploratory data analysis skills, with experience using tools such as pandas, NumPy, matplotlib/Plotly, or equivalent libraries to discover and communicate patterns in data. • Experience with causal inference, experimental design, Bayesian methods, operations research, graph methods, or other mathematically rigorous techniques for extracting decision-relevant insight from complex data. • Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Operations Research, Physics, Economics, or a related quantitative field; equivalent practical experience is also valued. About us: Accuris, a company long-known for accelerating innovation in engineering workflows and supporting the vibrancy of the engineering community, launched in May 2023 as a standalone company. Accuris was formerly known as S&P Global’s Engineering Solutions division. The Company is valued for its standards content and workflow solutions like Engineering Workbench, Goldfire, Haystack and Parts Management Solutions. Under its previous owners, including S&P Global, IHS and IHS Markit, Accuris has been an integral part of the engineering ecosystem for more than 60 years. About Company Statement: Accuris delivers essential intelligence that powers decision making. We provide the world’s leading organizations with the right data, connected technologies and expertise they need to move ahead. We think differently, combining the knowledge and resources of an established company with the unapologetic boldness of a startup. Our mission: build an evolvable knowledge and data platform that enables STEM professionals to unlock and deliver innovation to the world’s most complex problems. Accuris provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.