Data Scientist

As a Data Engineer/Scientist within the Global Sales Reporting Product Team, you are responsible to design and deliver production-grade data and analytics solutions that strengthen global sales reporting and decision-making through Agile delivery frame work. This is a cross-disciplinary role at the intersection of Data Science, Data Engineering, and Software Engineering. You will work with product, business, and technology partners to build scalable, end-to-end data products for dealer sales forecasting, retail and fleet analytics, reporting, and trusted sales data delivery.
The primary focus of this role is sales forecasting and predictive modeling for dealer sales. You will translate business needs into analytical formulations, work independently with strong ownership, and collaborate effectively as part of a product team. Success requires balancing model accuracy, resilient data pipelines, software quality, clear reporting, and disciplined data governance and quality practices.
Sales Forecasting & Predictive Modeling: Develop, implement, validate, and continuously improve forecasting and predictive models for dealer sales, including retail and fleet use cases. Apply appropriate time-series, statistical, machine learning, causal, and optimization techniques.
Retail & Fleet Analytics: Build analytical models and reusable data assets that help business partners understand retail and fleet sales patterns, market signals, dealer performance, forecast drivers, and exceptions.
Sales Reporting & Insights: Design and enhance trusted reports, dashboards, and visualizations that connect disparate sales data, highlight meaningful patterns, and communicate actionable insights to technical and non-technical stakeholders.
Data Pipelines & Engineering: Source, transform, test, and deliver reliable data pipelines that support models, reports, and downstream sales reporting products.
Full-Lifecycle Ownership: Own work from requirement discovery and data exploration through model development, integration, deployment, documentation, monitoring, and ongoing support. Proactively identify risks, dependencies, and opportunities for improvement.
Independent & Team-Based Delivery: Plan and execute assignments independently while contributing actively to a cross-disciplinary product team. Participate in technical design, estimation, code reviews, testing, troubleshooting, and shared delivery commitments.
MLOps, Reliability & Monitoring: Build and maintain production-ready workflows for model deployment, retraining, versioning, and monitoring. Establish proactive controls for data drift, bias, forecast accuracy, pipeline health, and operational reliability.
Data Governance & Quality: Embed data governance and quality controls across sales data products, including clear definitions, lineage, validation rules, issue management, documentation, and accountable data ownership.
Cross-Functional Collaboration: Partner with product leads, business stakeholders, data engineers, software engineers, and reporting teams to translate complex analytical concepts into practical, scalable solutions and informed business decisions.
Continuous Innovation: Stay current with advances in data science, data engineering, machine learning, analytics, and software delivery. Identify opportunities to improve the accuracy, efficiency, explainability, and robustness of the team’s solutions.
Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics.
Overall IT Experience: 5 to 6 years of relevant IT experience delivering data and analytics, reporting, or software solutions.
Data Science Experience: 2+ years of hands-on experience in a Data Scientist or closely related role, applying statistical, machine learning, forecasting, or optimization techniques to business problems.
Technical Skills: Hands-on experience with Python, SQL, data preparation , Data Build Tool ( DBT) , Data Fusion, exploratory analysis, model development, validation, and production implementation.
Data & Reporting Delivery: Experience working with data pipelines, data quality controls, analytical datasets, reports, dashboards, or APIs that support business-facing products.
Ownership & Collaboration: Demonstrated ability to take ownership, work independently, manage priorities, and collaborate effectively within cross-functional product and engineering teams.
Communication: Ability to translate sales or business problems into analytical formulations and explain model results, assumptions, limitations, and recommendations to non-analytics partners.

Work arrangement
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