Senior Data Scientist

About Lowe’sLowe’s Companies, Inc. (NYSE: LOW) is a FORTUNE 50 home improvement company serving approximately 16 million customer transactions a week in the United States. With total fiscal year 2023 sales of more than $86 billion, Lowe’s operates over 1,700 home improvement stores and employs approximately 300,000 associates. Based in Bengaluru, Lowe’s India develops innovative technology products and solutions and delivers business capabilities to provide the best omnichannel experience for Lowe’s customers. Lowe’s India employs over 4,200 associates across technology, analytics, merchandising, supply chain, marketing, finance and accounting, product management and shared services. Lowe’s India actively supports the communities it serves through programs focused on skill-building, sustainability and safe homes. For more information, visit, www.lowes.co.in.About the TeamWe are a dynamic and collaborative Data Science & Machine Learning team focused on driving strategic pricing decisions in the retail space. Our mission is to leverage data and advanced algorithms to optimize pricing strategies, enhance customer experiences, and maximize business outcomes. The team is a blend of data scientists, engineers, and domain experts who thrive on tackling complex challenges, deploying scalable solutions, and making data-driven decisions that have a direct impact on the business.Job Summary:We are looking for a passionate and skilled Senior Data Scientist who can drive impactful decisions through advanced modeling, optimization, and scalable deployments. This role is ideal for someone who thrives at the intersection of data science and engineering, working to develop, deploy, and scale predictive models that optimize pricing strategies and promotional effectiveness in the retail space. You will play a critical role in leveraging data to influence pricing decisions, increase profitability, and drive business impact.Roles & Responsibilities:Predictive Modeling & ForecastingDevelop and implement robust time series forecasting models and price elasticity models to optimize pricing strategies and promotions.Perform linear and non-linear curve fitting to capture demand patterns and pricing behaviors.Continuously refine models to improve forecast accuracy and business impact.Optimization & Dynamic PricingBuild and optimize multi-objective models to support price optimization initiatives, balancing revenue, margin, and inventory constraints.Utilize optimization techniques to inform dynamic pricing strategies, ensuring competitiveness and profitability in the market.Model Development & Feature EngineeringDesign and create features to improve model performance using advanced feature engineering techniques.Conduct end-to-end model development from exploratory data analysis (EDA) to model validation, ensuring solutions are production-ready.Leverage distributed computing (PySpark, Dask) to handle large-scale datasets efficiently.MLOps & Model DeploymentDeploy models at scale using Python, GCP Vertex AI, and containerization tools (Kubernetes, Docker).Expose models as APIs using frameworks like FastAPI or Flask for real-time consumption by downstream applications.Orchestrate and automate pipelines using Airflow, ensuring seamless model updates and retraining.Model Governance & MonitoringImplement model management best practices, including version control, MLFlow tracking, and model registries for reproducibility.Monitor deployed models to ensure performance remains consistent and robust over time, addressing model drift where necessary.Apply model governance and compliance standards to align with industry best practices.Collaboration & Stakeholder ManagementWork closely with cross-functional teams, including product managers, engineers, and analysts, to translate business problems into data-driven solutions.Communicate complex analyses and insights to non-technical stakeholders, influencing pricing decisions and strategy.Years of Experience:5-8 yrsRequired Minimum Qualifications :Bachelor's Degree in Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field (or equivalent work experience in a related field) AND 4-8 years of experience in Predictive Analytics, Machine learning and Statistical modelling with Python, SQL AND GCP or any other cloud data technologiesORMaster's Degree in Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field AND 3-7 years of experience in Predictive Analytics, Machine learning and Statistical modelling with Python, SQL AND GCP or any other cloud data technologiesORPh.D. in Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field AND 2-5 year of experience in Predictive Analytics, Machine learning and Statistical modelling with Python, SQL AND GCP or any other cloud data technologiesSkill Set RequiredTechnical Skills:Strong programming skills in Python; experience with distributed computing (PySpark, BigQuery).Proficient in deploying models using Docker, Kubernetes, FastAPI/FlaskSolid understanding of MLOps practices, including MLFlow, feature stores, and model governanceGCP (especially Vertex AI), and orchestration tools like Airflow.Soft Skills:Excellent problem-solving skills, with the ability to navigate ambiguity.Strong communication skills, capable of translating technical insights into actionable business recommendations.Collaborative mindset, with a focus on driving measurable business impact.

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