Analytics Modeler
Ford Motor Company · Chennai, Tamil Nadu, India
- On site
- Full-time
- $120,000 / year
- Chennai, Tamil Nadu, India
Job highlights
- Analyze customer behavior to drive business growth.
- Develop AI/ML and conversational AI solutions.
- Collaborate with stakeholders for impactful analytics.
- Build BI dashboards and data pipelines.
- Innovate with new technologies like Generative AI.
About the role
Analytics Modeler
This role is pivotal in understanding customer behavior and translating business challenges into intelligent analytical solutions. The ideal candidate will combine deep expertise in predictive modeling, AI/ML, and conversational AI with strong business engagement skills to deliver impactful, technology-driven solutions that enhance customer experience and drive business growth.
Responsibilities
- Strategic Business Partnership: Collaborate closely with business stakeholders to deeply understand business challenges, define key performance indicators (KPIs), and translate complex customer-focused questions into clear, actionable analytical requirements. Act as a trusted analytics advisor to business teams, proactively identifying opportunities where data and AI can solve real business problems.
- Customer Insight & Propensity Modeling: Conduct in-depth analysis of customer behavioral data to identify trends, patterns, and opportunities. Design, develop, and deploy propensity models (e.g., purchase propensity, churn prediction, upsell/cross-sell likelihood, lead conversion likelihood) using advanced statistical and machine learning techniques. Translate model outputs into actionable business recommendations.
- Agentic AI Application Development & Optimization: Lead the design, development, and continuous improvement of AI-powered solutions to enhance customer engagement and automate customer interactions. Collaborate with product and technology teams to integrate Agentic AI capabilities, define conversation flows, and measure chatbot effectiveness through relevant KPIs.
- BI & Data Product Development: Build, maintain, and optimize interactive dashboards and reports using BI tools (e.g., Power BI, Qlik Sense) to visualize Customer performance metrics. Develop robust and scalable data pipelines to ensure timely, accurate, and reliable data flow from various customer data sources into analytical platforms.
- Process Efficiency & Innovation: Continuously identify opportunities to enhance data collection, processing, analysis, and insight delivery workflows. Proactively research, evaluate, and implement new tools, technologies, and methodologies — including Generative AI — to increase efficiency, accuracy, and depth of analytical capabilities.
- Cross-functional Collaboration: Work seamlessly with data engineers, IT, marketing, and product teams to ensure data consistency, integrate analytical solutions, and drive data-driven decision-making across the organization. Effectively communicate complex analytical findings and model outputs to both technical and non-technical audiences.
Qualifications
- MBA/Masters in a quantitative discipline like Mathematics/Statistics/Operations Research/Computer Science/Economics/Engineering or B-Tech in any related engineering discipline.
- 3+ years of experience in Marketing Analytics with a strong foundation in analytical thinking and problem-solving.
- Proven technical expertise in AI/ML modeling, including propensity and predictive modeling, along with hands-on experience in Agentic AI applications development and Conversational AI.
- Proficiency in Python and SQL, experience with GCP and BigQuery, and capability in building dashboards using Power BI or Qlik Sense.
- Strong understanding of Digital Marketing and Customer Analytics, combined with data handling and ETL processes.
- Demonstrated ability to confidently engage with business stakeholders and deliver technology-driven solutions that create measurable business impact.
Key skills/competency
- Predictive Modeling
- AI/ML
- Conversational AI
- Business Partnership
- Customer Insight
- Agentic AI
- BI Tools
- Data Pipelines
- Python
- SQL
Skills & topics
- Analytics Modeler
- Marketing Analytics
- Predictive Modeling
- AI
- Machine Learning
- Conversational AI
- Customer Analytics
- Python
- SQL
- GCP
- BigQuery
- Power BI
- Qlik Sense
- ETL
- Data Pipelines
- Business Intelligence
- Ford Motor Company
- Analytics
- Data Science
How to get hired
- Tailor your resume: Highlight your experience in marketing analytics, AI/ML, and conversational AI, specifically mentioning Python, SQL, GCP, BigQuery, Power BI, or Qlik Sense.
- Showcase business impact: Quantify your achievements in previous roles, demonstrating how your analytical solutions drove measurable business results and enhanced customer experience.
- Prepare for technical questions: Be ready to discuss your experience with propensity modeling, Agentic AI, data pipelines, and ETL processes.
- Demonstrate stakeholder engagement: Practice explaining complex analytical concepts and model outputs clearly to both technical and non-technical audiences.
- Research Ford's values: Understand Ford's commitment to innovation and customer-centricity to align your responses during the interview process.
Technical preparation
Practice Python/SQL for data manipulation and analysis.,Build sample propensity models using ML libraries.,Develop a small chatbot interaction flow.,Create a sample dashboard in Power BI/Qlik.
Behavioral questions
Describe a complex business problem solved with data.,How do you translate business needs into analytics requirements?,Explain a technical concept to a non-technical audience.,How do you handle conflicting stakeholder priorities?
Frequently asked questions
- What specific AI/ML models are most relevant for this Analytics Modeler role at Ford?
- For this Analytics Modeler position at Ford, the most relevant AI/ML models include propensity models for purchase prediction, churn prediction, upsell/cross-sell likelihood, and lead conversion likelihood. Experience with Agentic AI applications and Conversational AI is also crucial.
- What are the key technical skills required for the Analytics Modeler job at Ford Motor Company?
- Key technical skills for this Analytics Modeler role at Ford include proficiency in Python and SQL, hands-on experience with cloud platforms like GCP and BigQuery, and experience in building dashboards using BI tools such as Power BI or Qlik Sense. Knowledge of data handling and ETL processes is also essential.
- How important is business engagement and stakeholder communication for this role at Ford?
- Business engagement and stakeholder communication are critically important for this Analytics Modeler role at Ford. You will act as a trusted analytics advisor, translating business challenges into analytical requirements and communicating complex findings to diverse audiences.
- What kind of customer data will an Analytics Modeler work with at Ford?
- An Analytics Modeler at Ford will likely work with a wide range of customer behavioral data to identify trends, patterns, and opportunities. This includes data relevant to purchase propensity, churn prediction, customer engagement, and digital marketing interactions.
- Does Ford Motor Company encourage innovation in analytics roles?
- Yes, Ford Motor Company encourages innovation. The job description explicitly mentions a responsibility to 'Continuously identify opportunities to enhance data collection, processing, analysis, and insight delivery workflows' and to 'Proactively research, evaluate, and implement new tools, technologies, and methodologies — including Generative AI'.
- What is the educational background typically expected for an Analytics Modeler at Ford?
- Ford typically expects an MBA/Masters in a quantitative discipline such as Mathematics, Statistics, Operations Research, Computer Science, Economics, or Engineering, or a B-Tech in any related engineering discipline for this Analytics Modeler role.
- How does this Analytics Modeler role contribute to Ford's business objectives?
- This Analytics Modeler role is key to understanding customer behavior, enhancing customer experience, and driving business growth. By developing intelligent analytical solutions and AI-powered tools, the role directly impacts marketing effectiveness and customer engagement strategies.