Assoc Dir-AI Science

Cellco Partnership; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Verizon is seeking an Associate Director of AI Science to bridge advanced analytics with business strategy in the Home and Mobility units. You will own propensity modeling, microsegmentation, and ROI-focused initiatives, guiding data scientists and engineers while delivering measurable growth and value across customer lifecycles.

This leadership role requires 8+ years in data science with 3+ in product or business-facing roles, and a strong telecom or tech background.

Qualifications

• 8+ years in data science, analytics, or AI strategy.
• 3+ years in leadership, product management, or business-facing role.
• Deep telecom/Home and Mobility domain knowledge.

Responsibilities

• Define and drive the AI strategy across Home and Mobility.
• Translate ML outputs into measurable business value and ROI.
• Lead cross-functional teams and mentor data scientists and AI engineers.

Skills

Data science leadership

Propensity modeling

Segmentation

ML/AI fluency

Stakeholder management

Education

Master's degree in Data Science or related

MBA preferred

Tools

PyTorch

scikit-learn

Job description

When you join Verizon

You want more out of a career. A place to share your ideas freely - even if they're daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love - driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together - lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife.

The Associate Director of AI Science is a highly strategic, business-facing leadership role dedicated to driving commercial value across our Home and Mobility verticals. Sitting at the intersection of advanced machine learning and business strategy, this role focuses on leveraging propensity modeling and dynamic microsegmentation to unlock new revenue streams, optimize the customer lifecycle, and elevate customer lifetime value.

You will act as the crucial bridge between our technical AI Science teams and the Home and Mobility business units, ensuring that complex AI and data science capabilities are translated into adopted, scalable solutions that deliver measurable ROI and continuous business growth.

Key Responsibilities

Business Strategy & Commercial Value Creation

• Monetize AI Capabilities: Lead the strategic application of advanced propensity models (e.g., likelihood-to-buy, churn prediction, next-best-action, cross-sell/up-sell) to drive tangible growth and retention in the Home and Mobility sectors.
• Define Microsegmentation Strategy: Guide the development of highly granular, behavioral microsegments to personalize marketing efforts, optimize pricing strategies, and tailor product offerings in real-time.
• ROI Accountability: Establish frameworks to measure the commercial impact of deployed AI models. Define, track, and report on key performance indicators (KPIs) that demonstrate direct value creation, revenue lift, and cost optimization.
• Lifecycle Optimization: Embed AI-driven insights into critical touchpoints along the customer lifecycle, ensuring predictive interventions are timed perfectly with high-impact trigger events.

Stakeholder Management & AI Adoption

• Executive Liaison: Serve as the primary strategic advisor and translator between the AI Science function and Home/Mobility business leaders (Marketing, Product, Sales, and Customer Experience).
• Drive Business Adoption: Champion the operationalization of AI models. Overcome organizational silos and resistance by demonstrating clear business value, securing buy-in, and embedding AI tools directly into end-user workflows.
• Storytelling with Data: Translate highly technical machine learning concepts, model performance metrics, and statistical outputs into clear, compelling narratives and strategic recommendations for senior executive leadership.
• Needs Translation: Proactively identify business pain points within Home and Mobility and translate these into actionable data science projects, defining clear modeling requirements for the technical teams.

Technical Oversight & Cross-Functional Leadership

• Model Lifecycle Strategy: Oversee the business strategy for models deployed in high-impact areas, ensuring continuous optimization, accuracy, and relevance to shifting market dynamics.
• Pipeline Integration: Partner closely with Data Engineering, MLOps, and IT teams to ensure models are seamlessly deployed into production environments and marketing automation platforms.
• Team Mentorship: Provide strategic direction and business-context mentorship to data scientists and AI engineers, ensuring their technical execution remains tightly aligned with corporate objectives.

Core Competencies & Qualifications

Experience & Education

• Education: Master’s degree in Data Science, Computer Science, Statistics, Business Analytics, or a related field (an MBA with a strong quantitative focus is highly preferred).
• Industry Experience: 8+ years of experience in data science, advanced analytics, or AI strategy, with a minimum of 3 years in a leadership, product management, or business-facing role.
• Domain Knowledge: Deep understanding of the telecommunications, broadband, or consumer technology industries, specifically within Home (e.g., fiber, smart home, IoT) and Mobility (e.g., 5G, wireless services) verticals.

Technical & Business Skills

• AI/ML Fluency: Strong foundational understanding of machine learning algorithms, deep learning, NLP, and LLM architecture. Must be highly conversant in how techniques utilizing frameworks like PyTorch and scikit-learn are applied to business problems, even if not writing production code daily.
• Advanced Propensity & Segmentation: Proven track record of designing, evaluating, and strategically deploying end-to-end propensity models and clustering techniques for large-scale customer bases.
• Data Stack Familiarity: Ex