Head of Data Science

Xformics Inc. is a global product and strategic consulting company with a presence in the USA, Canada, Europe and India. With a leadership team derived from the best of GE, Oracle and IBM, Xformics advanced AI and digital products solve niche problems across various industry verticals, be it predicting the failure of Jet engines or optimizing the supply chain focusing on order fulfilment end to end life cycle. Our clients include many Fortune 100 companies in Banking, Retail, Manufacturing, and Healthcare. Join us and help define the technologies that are shaping the world of tomorrow.

Designation: Head of Data Science

Department: Products & Services

Reports To: Director, Products & Services

Experience Level: 10–15 Years

Location: India (Remote)

Position Overview

Xformics is seeking a visionary and hands-on Head of Data Science to drive the next generation of AI-powered products and intelligent business solutions. This role will be instrumental in defining the company's Data Science, Artificial Intelligence, Machine Learning, and Advanced Analytics strategy while leading the development of scalable, enterprise-grade AI products.

The ideal candidate combines deep expertise in Data Science, Machine Learning, Cloud Technologies, MLOps, and Product Engineering with strong business acumen and leadership capabilities. The individual will lead cross-functional teams in building innovative AI solutions that solve complex business challenges across Retail, Finance, and other enterprise domains.

This is a highly strategic yet hands-on leadership role requiring ownership from ideation and architecture through deployment, optimization, and business adoption.

Role Purpose

As the Head of Data Science, you will own the vision, architecture, and execution of AI-driven products and platforms. You will work at the intersection of business strategy, product innovation, machine learning, cloud engineering, and customer engagement to create sustainable competitive advantages through data and artificial intelligence.

You will lead large-scale AI initiatives, mentor high-performing teams, influence executive stakeholders, and ensure successful delivery of production-grade AI solutions that generate measurable business outcomes.

Key Responsibilities

AI & Data Science Strategy

• Define and drive the organization's AI, Machine Learning, and Advanced Analytics vision.
• Build multi-year AI product roadmaps aligned with organizational goals and market opportunities.
• Identify emerging technologies and innovation opportunities in AI, Generative AI, and Intelligent Automation.
• Establish data-driven decision-making frameworks across products and services.

AI Product Leadership

• Lead the conceptualization, design, architecture, and deployment of enterprise AI products.
• Translate business challenges into scalable data science and machine learning solutions.
• Own the complete AI product lifecycle from problem formulation through model development, deployment, monitoring, optimization, and ongoing improvement.
• Partner with Product Management teams to deliver commercially successful AI capabilities.

Machine Learning & Advanced Analytics

• Design and implement sophisticated machine learning solutions
• Drive experimentation frameworks, model evaluation strategies, and continuous performance improvement.

Generative AI & Emerging Technologies

• Lead the adoption and integration of Generative AI technologies within products and services.
• Design enterprise-grade solutions leveraging:
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Conversational AI
• Knowledge-Based Systems
• Evaluate emerging AI technologies and develop practical business applications.

Data Engineering & Large-Scale Analytics

• Architect scalable data platforms and machine learning pipelines capable of supporting enterprise workloads.
• Develop data strategies for structured, semi-structured, and unstructured datasets.
• Drive large-scale analytics initiatives using distributed processing frameworks.
• Collaborate closely with engineering teams on data architecture, integration, and governance.

Cloud & MLOps Leadership

• Lead AI platform development across Azure, AWS, and Google Cloud ecosystems.
• Build robust MLOps practices covering and drive adoption of cloud-native AI architectures and best practices.

Enterprise Solution Architecture

• Establish architectural standards for AI products and platforms.
• Partner with Engineering, DevOps, Security, and Product teams to ensure successful production deployment.

Leadership & Talent Development

• Build, mentor, and lead a high-performing team of Data Scientists, Machine Learning Engineers, and AI Specialists.
• Establish technical excellence standards and best practices.
• Promote a culture of innovation, experimentation, ownership, and continuous learning.
• Drive hiring and talent development initiatives.

Executive Stakeholder Management

• Act as a trusted advisor to executive leadership and key clients.
• Present AI strategies, architectures, business cases, and solution outcomes to senior stakeholders.
• Lead customer workshops, product demonstrations, and strategic AI discussions.
• Translate complex technical concepts into clear business value propositions.

Required Qualifications

Experience

• 12-15+ years of experience in Data Science, Artificial Intelligence, Machine Learning, Analytics, or related disciplines across Retail/Financial Services domain.
• Proven experience leading enterprise-scale AI initiatives and teams.
• Demonstrated success delivering production-grade AI and ML solutions.
• Experience building AI products from concept to commercialization.

Technical Expertise

Strong hands-on experience with:

Programming

• Python
• Java (preferred)
• SQLData Science & Machine Learning
• Statistical Modeling
• Machine Learning Algorithms
• Advanced Analytics
• Deep Learning
• NLP
• Predictive Modeling
• Recommendation SystemsData Science Frameworks
• Scikit-Learn
• NumPy
• Pandas
• TensorFlow
• PyTorch
• KerasData Platforms
• Spark
• PySpark
• Databricks
• Data Warehousing TechnologiesCloud Technologies
• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)Generative AI
• Large Language Models
• RAG Architectures
• AI Agents
• GenAI Application DevelopmentMLOps
• MLflow
• Kubeflow
• Model Monitoring
• CI/CD Pipelines
• Experiment ManagementLeadership Competencies
• Strategic Thinking
• Product Mindset
• Business Acumen
• Technical Leadership
• Executive Presence
• Client Relationship Management
• Stakeholder Influence
• Innovation Leadership
• Cross-Functional CollaborationEducational Qualifications

Bachelor's or Master's degree in:

• Computer Science
• Data Science
• Statistics
• Mathematics
• Engineering
• Artificial IntelligenceA PhD or advanced specialization in AI, Machine Learning, Statistics, or related fields will be considered an advantage.