Data Scientist

Foom is a fast-growing company focused on retail, e-commerce, and consumer analytics, leveraging data-driven strategies to optimize business performance. We are looking for a Data Scientist who can help turn complex datasets into actionable insights, optimize models for forecasting and personalization, and support business decisions with data-backed strategies.

Job Description 1. Data Analysis & Insights Analyze sell-in, sell-out, stock availability, and transaction data from multiple sources (Odoo, PostgreSQL, Field Officer Apps, flagship store APIs, Customers). Build dashboards and reports to track key business metrics like sales performance, customer segmentation, and product trends. Conduct exploratory data analysis (EDA) to identify patterns and insights that drive business growth. Collaborate with marketing, finance, and operations teams to support decision-making with data-driven insights. 2. Predictive Analytics & Forecasting Develop predictive models for demand forecasting, pricing optimization, and inventory planning. Use time series forecasting to analyze sales trends and mitigate fluctuations due to promotions or seasonality. Implement customer behavior models to understand purchasing patterns and recommend targeted marketing strategies. 3. Data Cleansing, Normalization & Transformation Perform data cleansing, standardization, and normalization to improve data quality and consistency. Work with structured and unstructured data, handling missing values, duplicates, and outliers. Optimize data processing pipelines for scalability and efficiency. 4. Machine Learning & AI Implementation Develop and deploy machine learning models for customer segmentation, churn prediction, and recommendation systems. Implement clustering, classification, regression, and NLP techniques to extract meaningful insights. Optimize models for real-time decision-making in pricing, promotions, and customer targeting. 5. Data Engineering & Automation Design and maintain ETL pipelines to ingest, process, and store data efficiently. Automate data workflows and integrate external data sources (APIs, third-party data, and cloud databases). Collaborate with Data Engineers to optimize query performance and data warehouse architecture. 6. A/B Testing & Experimentation Design and execute A/B tests to evaluate marketing campaigns, pricing strategies, and product placements. Analyze customer behavior and conversion metrics to optimize user experience. Provide statistical validation of business hypotheses and guide decision-making based on test results.

General Qualification Minimum Requirement Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. 3+ years of experience in data science, analytics, or machine learning. Proficiency in Python, R, or SQL for data analysis and model development. Experience with data visualization tools (Tableau, Metabase, Power BI, or similar). Strong knowledge of machine learning algorithms (classification, clustering, regression, deep learning). Experience working with large datasets and databases (PostgreSQL, MySQL, MongoDB, Google BigQuery, Snowflake). Familiarity with cloud platforms (AWS, GCP, or Azure) for data storage and computing. Strong problem-solving skills and business acumen to translate data into actionable insights. Preferred Qualifications: Experience in retail, FMCG, or e-commerce analytics. Knowledge of marketing analytics, customer segmentation, and campaign performance analysis. Familiarity with big data frameworks (Spark, Hadoop, Airflow). Understanding of natural language processing (NLP) for sentiment analysis and customer feedback interpretation. Hands-on experience with A/B testing and causal inference methods.