Data Scientist

As a leading food and drug retailer in the United States, Albertsons Companies, Inc. operates over 2,200 stores across 35 states and the District of Columbia. We build and shape technology solutions that solve customers’ problems every day, making things easier for them when they shop with us online or in a store. We have made bold, strategic moves to migrate and modernize our core foundational capabilities, positioning ourselves as the first fully cloud-based grocery tech company in the industry. Our success is built on a one-team approach, driven by the desire to understand and enhance the customer experience. By constantly pushing the boundaries of retail, we are transforming shopping into an experience that is easy, efficient, fun and engaging. At Albertsons Companies India, we're not just pushing the boundaries of technology and retail innovation, we're cultivating a space where ideas flourish and careers thrive. workforce and important to the next phase in the company’s technology journey to support millions of customers’ lives every day. At the Albertsons Companies India, we are raising the bar to grow across Technology & Engineering, AI, Digital and other company functions, and transform a 165-year-old American retailer. At Albertsons Companies India associates collaborate directly with international teams, enhancing decision-making processes and organizational agility through exciting and pivotal projects. Your work will make history and help millions of lives each day come together around the joys of food and inspire their well-being. Position Title: Advanced Data Scientist Collaborate with business and product stakeholders and build production grade models and machine learning algorithms on large-scale datasets Optimize inventory policies to reduce supply chain waste and opportunity cost related to holding excess inventory by maximizing the productivity of inventory Identify key drivers for out of stocks and address them using Machine Learning and Mathematical Optimization techniques to maximize in stock conditions at stores to provide the best customer experience Apply machine learning and mathematical optimization to increase productivity in ecommerce fulfillment operations and deliver the most optimum user experience on ecommerce website and loyalty mobile app Apply predictive modeling techniques and survival models to forecast various demands for operations and to deploy real time learning algorithms to optimize the forecasts Build models and algorithms to fuel growth initiatives for In Stock, Omni Channel, Digital and Merchandising Apply statistical analysis to detect anomaly in systems and outliers in operational metrics for operational excellence Convey machine learning solutions with intuitive visualizations and effective communications Collaborate with software development engineers to integrate models Experience Required: ~ Bachelors or master's or PhD degree in quantitative discipline: Computer Science, Engineering, Data Science, Math, Statistics or related mathematically intensive fields such as Experimental Physics or Chemical Engineering ~3+ years of industry experience in applying data science and modeling methodologies: regression model, survival model, ensemble modeling, NLP, recommendation algorithm, clustering, deep learning algorithm, experimental design (Multivariate/A-B testing) and nonparametric Bayesian modeling etc. ~ Experience with any of the following business analytics practices is preferred: ~ Supply chain analytics, including inventory optimization ~ Ecommerce fulfillment operations, pick path optimization ~3+ years of experience and proficiency in SQL, Python and/or Spark-ML ~3+ years of SQL development skills writing queries, transforming data, mining structured and unstructured data. ~2+ years of hands-on experience in building data science solutions and production-ready systems on big data platforms such as Databricks, Spark, Hadoop ~ Ability to write production-level code in Python ~ Experience with Snowflake, Azure Databricks is a strong plus ~ Building Recommendation/ personalization systems or foundational systems on apps & websites. Statistical and Experimental Modeling (regression modeling, survival modeling, experimental design multivariate/A-B testing) ML and AI (deep learning algorithms, NLP, nonparametric Bayesian modeling, ensemble modeling, recommendation algorithms, clustering) Mathematical and Applied Optimization (operations research, linear optimization, non-linear optimization, numerical search) SQL, Python Big Data and Tools (Spark-ML, Snowflake, Spark, Hadoop, Big Query) Production-level Python coding Data Engineering AI Methodologies (GenAI, Agentic AI)