Data Scientist with strong Azure

KAYGEN•Austin, TX

3d•Onsite

About The Position

KAYGEN is seeking a Data Scientist with strong Azure experience for a 6-month contract role in Austin, TX. This position involves leading the end-to-end development of sales forecasting and demand sensing models for CPG portfolios on Databricks (Azure). The role requires close collaboration with commercial, supply chain, and engineering teams to build ML solutions that enhance forecast accuracy, minimize inventory waste, and drive revenue growth. The ideal candidate will possess deep ML expertise, strong Python engineering skills, and a solid understanding of CPG market dynamics, with the ability to translate complex model outputs into actionable business recommendations.

Requirements

Master's or PhD in Statistics, CS, or related field (preferred)
8+ years in data science or applied ML roles
3+ years in CPG, FMCG, or retail analytics
Hands-on Databricks experience in production
Strong Python — pandas, PySpark, scikit-learn
Experience with Azure ML or Azure ecosystem
MLflow or equivalent experiment tracking tool
3+ years of experience in Databricks in production
5+ years of experience in Python — pandas, PySpark, scikit-learn
5+ years of experience with Azure ML or Azure ecosystem
3+ years of experience in MLflow or equivalent experiment tracking tool
5+ years of experience in Supervised, unsupervised machine learning algorithms, forecasting and inventory optimization
5+ years of experience in deep learning algorithms applying to solve forecasting, regression and classification problems
3+ years of experience in building ML models in CPG industry
Communicate effectively with internal and customer stakeholders
Strong interpersonal skills to build and maintain productive relationships with team members
Capability to troubleshoot and resolve issues efficiently.
Analytical mindset
Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
Provides regular updates, proactive and due diligent to carry out responsibilities

Nice To Haves

Statistical Analysis & Experimentation: A/B testing, causal inference, and hypothesis testing to measure the business impact of model improvements and pricing interventions.
SQL & Data Engineering Fundamentals: Advanced SQL on Delta Lake / Azure Synapse; ability to build lightweight feature pipelines without full data engineering support.
MLOps & CI/CD for ML: MLflow, GitHub Actions, or Azure DevOps pipelines to automate model retraining, evaluation gates, and deployment to Databricks Model Serving.
Data Visualisation & Storytelling: Power BI, Plotly, or Streamlit dashboards to communicate forecast accuracy and business KPIs to non-technical stakeholders.
Promotional & Trade Analytics: Modelling promotional uplift, baseline vs incremental volume splits, and trade spend ROI — key for CPG forecast decomposition
Team Leadership & Mentoring: Guide junior data scientists, run code reviews, define modelling standards, and represent the data science function in cross-functional forums.

Responsibilities

Lead end-to-end sales forecasting model development — from data sourcing and feature engineering through model training, validation, and productionisation on Databricks (Azure).
Design and maintain forecasting pipelines — at SKU, category, and regional hierarchy levels — incorporating POS data, promotional calendars, seasonality indices, and external signals (macroeconomic, weather).
Apply CPG domain knowledge — to model promotional uplift, new product introduction curves, product cannibalization, and retailer sell-in/sell-out dynamics into ML features and targets.
Operationalise ML models using MLflow on Databricks — manage the model registry, version control experiments, automate retraining schedules, and configure drift monitoring alerts.
Collaborate with commercial and supply chain teams — to translate forecast outputs into inventory recommendations, production planning inputs, and revenue growth strategies.
Define and enforce data science best practices — modelling standards, experiment documentation, code review guidelines, and reproducibility requirements across the team.
Mentor junior data scientists — conduct code reviews, lead knowledge-sharing sessions, support career development, and build a high-performance data science culture.
Communicate model insights and forecast accuracy — to senior stakeholders through dashboards, executive briefings, and written reports — making complex model behaviour accessible to business audiences.
Drive continuous model improvement — benchmark new algorithms, evaluate AutoML approaches, and run controlled experiments to improve MAPE, bias, and coverage metrics.
Partner with data and platform engineers — to ensure feature pipelines on Azure Data Lake / Delta Lake are reliable, scalable, and aligned with model refresh cadence requirements.