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.
