Website:
aarvian.com
Job details:
About Aarvian
Aarvian is a 'Process-First' AI transformation partner dedicated to operationalizing Data, ML, and AI for the global enterprise. We move beyond fragmented tools by aligning intelligence with business logic — mapping solutions from Process to Function, and ultimately to the Business Unit's P&L.
Our work is anchored on four core pillars:
• Domain-led Expertise — deep industry knowledge that grounds every AI solution in real-world business context.
• Comprehensive Lifecycle Ownership — end-to-end engagement from Advisory through to Adoption.
• Co-development Trust — we build with our clients, not just for them, fostering shared accountability.
• Ethical AI Foundation — a steadfast commitment to responsible, transparent, and fair AI practices.
At Aarvian, you will work alongside practitioners who bring rigorous thinking and genuine domain depth to some of the most complex AI challenges in global enterprise retail.
Role Overview
We are looking for an experienced Data Scientist with a strong machine learning background and hands-on experience in the retail domain. The ideal candidate will have a proven track record of building and deploying ML models that drive measurable business outcomes — including demand forecasting, pricing optimization, and customer analytics. You will work closely with business, engineering, and product teams to deliver data-driven solutions at scale.
Key Responsibilities
Demand Forecasting & Time Series Analysis
• Design and develop demand forecasting models using time series techniques such as ARIMA, SARIMA, Prophet, LSTM, and Temporal Fusion Transformers.
• Incorporate external signals (promotions, seasonality, holidays, market trends) to improve forecast accuracy.
• Build and maintain automated forecasting pipelines for SKU-level and category-level demand planning.
• Collaborate with supply chain and inventory teams to translate forecasts into actionable replenishment decisions.
Price Elasticity & Pricing Analytics
• Develop price elasticity models to quantify the sensitivity of consumer demand to price changes across product categories.
• Support dynamic pricing strategies by building models that recommend optimal price points to maximize revenue and margins.
• Conduct promotional lift analysis and markdown optimization using regression and causal inference techniques.
• Collaborate with the commercial and category management teams to embed pricing insights into business workflows.
Customer Analytics
• Build customer segmentation models (RFM, clustering, propensity models) to enable targeted marketing and personalization.
• Develop churn prediction and customer lifetime value (CLV) models to support retention strategies.
• Design and analyze A/B experiments to evaluate the impact of marketing campaigns and product changes.
• Build recommendation systems to improve cross-sell, upsell, and product discovery experiences.
General Data Science & MLOps
• Own the end-to-end model lifecycle — from ideation and experimentation to deployment, monitoring, and retraining.
• Ensure model performance, fairness, and explainability across production systems.
• Work with data engineers to define feature pipelines and maintain high data quality standards.
• Communicate findings and model insights to both technical and non-technical stakeholders through clear visualizations and presentations.
Required Qualifications & Skills
Education
• Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
Experience
• 3–5 years of hands-on experience in applied data science or machine learning roles.
• Demonstrated experience working within the retail or e-commerce domain is strongly preferred.
Technical Skills
• Proficiency in Python (pandas, scikit-learn, statsmodels, PyTorch/TensorFlow).
• Strong knowledge of time series forecasting methods and libraries (Prophet, statsmodels, sktime, Darts).
• Proficiency in gradient boosting frameworks, particularly XGBoost, for structured/tabular ML tasks such as churn prediction, price elasticity, and propensity modelling.
• Experience with causal inference and econometric modelling for pricing and elasticity.
• Familiarity with ML deployment tools (MLflow, Docker, REST APIs) and cloud platforms (AWS, GCP, or Azure).
• Proficiency in SQL and experience working with large-scale datasets.
• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib/Seaborn.
Soft Skills
• Strong problem-solving mindset with the ability to translate business questions into analytical frameworks.
• Excellent communication skills — able to present complex findings to business stakeholders clearly.
• Ability to manage multiple projects and priorities in a fast-paced environment.
Good to Have
• Experience with real-time or near-real-time ML inference pipelines.
• Knowledge of retail-specific data assets: POS data, loyalty data, basket data, web clickstream.
• Exposure to graph-based models or deep learning for recommendation systems.
• Familiarity with dbt, Airflow, or similar data orchestration tools.
• Published research or contributions to open-source ML projects.
What We Offer
• Opportunity to work on high-impact, real-world ML problems at scale.
• Collaborative and data-driven culture with strong leadership support.
• Access to rich retail datasets and modern ML infrastructure.
• Competitive compensation and career growth opportunities.
Click on Apply to know more.