JOBWAVE VERIFIED

Data Scientist — Global Sales Reporting Product Team

Role Title: Data Scientist / Data Engineer-Scientist
Company: Not specified
Team: Global Sales Reporting Product Team
Location: Chennai, Tamil Nadu, India
Work Mode: On-site
Employment Type: Not specified
Overall IT Experience: 5–6 years of relevant experience
Data Science Experience: 2+ years of hands-on experience
Education: Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, or Mathematics
Salary: Not specified
Job ID: Not specified

Core Technology Stack

• Programming: Python, SQL
• Data Transformation: dbt (Data Build Tool)
• Data Integration: Data Fusion
• Data Science: Statistical modeling, machine learning, time-series forecasting, predictive analytics, optimization
• Data Engineering: Data pipelines, data preparation, analytical datasets, data quality
• Reporting: Dashboards, visualizations, sales reporting, business insights
• Production ML: Model deployment, retraining, versioning, monitoring, MLOps
• Governance: Data lineage, validation rules, documentation, ownership, quality controls
• Delivery Practices: Agile, testing, code reviews, technical design, cross-functional collaboration

Specific cloud platforms, BI tools, ML libraries, and orchestration technologies are not specified.

────────────────────

JOBWAVE INSIGHTS

1. Who Is This Role For?

This role is suited to an experienced Data Scientist who combines predictive modeling with data engineering and production software delivery.

The primary focus is dealer sales forecasting, including retail and fleet sales. This is not purely a research-oriented data science position: the candidate is expected to build reliable data pipelines, deliver reports and dashboards, deploy models, monitor their performance, and maintain data quality throughout the product lifecycle.

The strongest candidate will have experience taking analytical solutions from business requirements and exploratory analysis through production deployment and ongoing monitoring.

2. Key Skills

Skill	Priority	What You Need to Demonstrate
Python	Critical	Data preparation, modeling, validation, production implementation
SQL	Critical	Data extraction, transformation, joins, aggregations, analytical datasets
Sales Forecasting	Critical	Forecasting dealer retail and fleet sales
Statistical Modeling	Critical	Statistical methods and predictive analysis
Machine Learning	Critical	Model development, evaluation, and improvement
Time-Series Analysis	Critical	Forecasting trends, seasonality, and demand patterns
dbt	Explicitly listed	Data transformations and maintainable data models
Data Fusion	Explicitly listed	Data integration and pipeline-related work
Data Pipelines	High	Reliable, tested data delivery
Model Validation	High	Forecast accuracy, assumptions, limitations, and evaluation
MLOps	High	Deployment, retraining, versioning, and monitoring
Data Quality and Governance	High	Validation, lineage, definitions, issue management
Reporting and Visualization	High	Dashboards and actionable business insights
Communication	High	Explaining model results to non-technical stakeholders
Agile and Code Reviews	High	Reliable team-based software delivery

3. Priority Skills

Highest priority

• Python and SQL
• Time-series forecasting and predictive modeling
• Statistical analysis and machine learning
• Model validation and forecast accuracy measurement
• Data preparation and exploratory data analysis
• Translating business requirements into analytical problems

Strong advantages

• dbt and Data Fusion
• Production data pipelines and API integrations
• MLOps, model monitoring, and automated retraining
• Retail, fleet, automotive, dealer, or sales analytics
• Data governance, lineage, and quality controls
• Dashboard development and business reporting

────────────────────

JOB DETAILS

Role Overview

You will work within a global sales reporting product team to develop end-to-end analytical and data products that support dealer sales forecasting, retail and fleet analytics, reporting, and reliable sales data delivery.

The role combines three disciplines:

1. Data Science: Build and improve forecasting and predictive models.
2. Data Engineering: Develop tested pipelines and trustworthy analytical datasets.
3. Software Engineering: Deliver production-ready solutions with monitoring, documentation, testing, and ongoing support.

Key Responsibilities

1. Sales Forecasting and Predictive Modeling

• Develop, implement, validate, and improve dealer sales forecasting models.
• Support both retail and fleet sales use cases.
• Apply appropriate time-series, statistical, machine learning, causal, and optimization techniques.
• Evaluate forecast accuracy and improve model reliability.

2. Retail and Fleet Analytics

• Analyze sales trends, market signals, and dealer performance.
• Identify forecast drivers, patterns, anomalies, and exceptions.
• Create reusable analytical models and datasets for business teams.

3. Sales Reporting and Insights

• Develop and enhance reports, dashboards, and visualizations.
• Combine data from multiple sources into trusted reporting outputs.
• Communicate meaningful findings and actionable recommendations to business stakeholders.

4. Data Engineering and Pipelines

• Source, transform, test, and deliver data.
• Build reliable pipelines supporting models, reports, and downstream products.
• Implement data validation and quality checks.

5. Full-Lifecycle Ownership

• Gather requirements and explore data.
• Develop, validate, and integrate models.
• Deploy and document solutions.
• Monitor performance and provide ongoing support.
• Identify risks, dependencies, and opportunities for improvement.

6. MLOps and Production Reliability

• Maintain model deployment and retraining workflows.
• Manage model versioning and monitoring.
• Monitor data drift, potential bias, forecast accuracy, and pipeline health.
• Improve operational reliability and production readiness.

7. Data Governance and Quality

• Define data validation rules and consistent business definitions.
• Maintain data lineage and documentation.
• Manage data quality issues and ownership.
• Ensure analytical outputs remain trustworthy and reproducible.

8. Cross-Functional Collaboration

• Work with product managers, business stakeholders, data engineers, software engineers, and reporting teams.
• Participate in technical design, estimation, code reviews, testing, and troubleshooting.
• Explain analytical assumptions, limitations, and recommendations clearly.

────────────────────

REQUIREMENTS

Technical Requirements

• Hands-on Python and SQL experience.
• At least two years of practical data science or closely related experience.
• Experience with statistical methods, machine learning, forecasting, or optimization.
• Experience with data preparation and exploratory data analysis.
• Experience with dbt and Data Fusion, as explicitly listed.
• Experience developing, validating, and implementing models in production.
• Experience with data pipelines, analytical datasets, reports, dashboards, or APIs.
• Ability to establish data quality controls and support reliable data delivery.

Experience Requirements

The description establishes two separate experience expectations:

• Overall IT: 5–6 years of relevant experience.
• Data Science: At least 2 years of hands-on experience.

A candidate should be prepared to demonstrate both overall professional experience and specific ownership of data science projects.

Professional Requirements

• Independent problem-solving and ownership.
• Effective prioritization and delivery.
• Cross-functional teamwork.
• Clear communication with technical and business stakeholders.
• Ability to explain model assumptions, results, limitations, and recommendations.

────────────────────

QUALIFICATIONS

• Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, or Mathematics.
• 5–6 years of relevant IT experience.
• 2+ years in data science or a closely related discipline.
• Experience delivering production-grade data and analytics solutions.
• Willingness to work on-site in Chennai.

Not specified: Salary range, employer name, specific cloud provider, preferred BI platform, exact ML framework, and employment contract type.

────────────────────

POTENTIAL GAPS TO CHECK

Before applying, assess your experience in these areas:

1. Forecasting depth: Have you built and evaluated time-series forecasting models, rather than only classification or regression models?
2. Business forecasting: Can you connect forecast results to dealer sales, retail, fleet, demand, or similar business metrics?
3. Production deployment: Have your models been deployed and monitored in a real production environment?
4. dbt and Data Fusion: Do you have hands-on experience with both tools? They are specifically named in the qualifications.
5. Data engineering: Can you build, test, and maintain data pipelines, not just perform analysis on prepared datasets?
6. MLOps: Have you handled model versioning, retraining, drift monitoring, and production reliability?
7. Governance: Can you demonstrate data quality validation, lineage, documentation, and issue management?
8. Experience threshold: Do you meet the 5–6 years of overall IT experience and 2+ years of data science experience?

The description does not specify whether every named tool is a strict screening requirement, so confirm the team's expectations during recruitment.

────────────────────

INTERVIEW PREPARATION

Phase 1 — Python, SQL, and Data Preparation

Python topics

• Pandas and NumPy
• Missing values, outliers, and data cleaning
• Feature engineering and data transformations
• Writing reusable, testable code
• Working with large datasets efficiently

SQL topics

• Joins, CTEs, and window functions
• Aggregations and analytical queries
• Date-based calculations
• Deduplication and data quality checks
• Query optimization and troubleshooting

Phase 2 — Statistics and Forecasting

Prepare to explain:

• Trend, seasonality, and cyclical patterns
• Time-series train/test splits and data leakage
• Moving averages and exponential smoothing
• ARIMA/SARIMA and other appropriate forecasting methods
• Regression and machine learning approaches
• Forecast bias and error metrics such as MAE, RMSE, and MAPE
• Forecasting with sparse, volatile, or incomplete data
• Incorporating external variables and business drivers
• Comparing a baseline model with a more complex model

Be prepared to justify why a particular forecasting method fits a business problem. These are suggested preparation topics, not confirmed technologies used by the employer.

Phase 3 — Data Engineering and dbt

Focus on:

• Designing reliable ETL/ELT pipelines
• Data transformation and reusable analytical models
• dbt models, sources, tests, documentation, and dependencies
• Data validation and reconciliation
• Incremental data processing
• Handling failed pipelines and late-arriving data
• Data lineage and trustworthy business definitions
• Integrating datasets for reporting and model training

Also prepare to discuss your hands-on experience with Data Fusion and how it fits into data integration workflows.

Phase 4 — MLOps and Production Delivery

Review:

• Packaging and deploying a model
• Model versioning and reproducibility
• Scheduled retraining and evaluation
• Data drift versus model performance degradation
• Monitoring forecast accuracy over time
• Alerting, logging, and pipeline health
• Testing, documentation, and rollback strategies
• Model explainability and bias checks
• Agile delivery, code reviews, and production troubleshooting

Final Interview Focus

Expect the strongest discussion to center on how you turn a business forecasting problem into a reliable production solution.

Be ready to walk through one complete project:

1. Business problem and success criteria.
2. Data sources, quality issues, and exploratory analysis.
3. Feature engineering and model selection.
4. Validation strategy and forecast metrics.
5. Data pipeline and production integration.
6. Monitoring, retraining, and ongoing improvements.
7. Business impact and communication of limitations.

────────────────────

PORTFOLIO PROJECT RECOMMENDATION

Dealer Sales Forecasting Pipeline

Build a portfolio project that predicts monthly sales for multiple dealers using historical sales and optional external variables.

Suggested features

• Python and SQL for data preparation.
• Exploratory analysis of sales trends and seasonality.
• Baseline and advanced time-series models.
• Separate forecasts by dealer and sales category.
• Evaluation using MAE, RMSE, and an appropriate percentage-based metric.
• dbt transformations and automated data-quality tests.
• A repeatable forecasting pipeline.
• A dashboard showing actual sales, predicted sales, forecast errors, and exceptions.
• Model performance monitoring and a retraining plan.

Use synthetic or publicly available data, and clearly distinguish simulated results from real business outcomes.

Why this project helps: It demonstrates the combination of forecasting, data engineering, model validation, reporting, and production thinking that defines this position.

────────────────────

FINAL RECOMMENDATION

Overall fit: Strong for a Data Scientist with practical forecasting experience and a data engineering mindset.

This role is a particularly good match if you have experience with Python, SQL, time-series analysis, production ML, and data pipelines. It is less suitable for candidates whose experience is limited to notebooks, basic reporting, or model experimentation without deployment and monitoring.

Application priority: Before applying, verify your experience against the two key thresholds—5–6 years overall IT and 2+ years in data science—and prepare concrete examples involving forecasting, dbt, Data Fusion, data quality, and production deployment.