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I. Detailed Role Description
We are looking for a highly skilled and visionary Data Science Team Manager (AI/ML) to lead a team of data scientists and machine learning engineers. This role is responsible for driving AI/ML initiatives that solve complex business problems, building scalable models and ensuring successful deployment into production systems. The ideal candidate combines hands-on technical expertise with strong leadership, business acumen and the ability to align data science strategy with organizational goals.
II. Roles And Leadership & Vision
- Develop and execute a comprehensive data strategy that integrates analytics and data science to drive business outcomes, such as revenue growth, operational efficiency and customer satisfaction.
- Translate business objectives into data-driven initiatives that deliver measurable value across departments.
- Lead Digital transformation for LTSI organization with strategic forecast on LHT vision 2030.
- Define team goals, set priorities and allocate resources effectively.
- Foster a culture of innovation, collaboration and continuous learning.
- Provide technical guidance and ensure best practices in AI/ML development.
- Initiate Hackathons and Capability Roadshows to find solutions to address LH's global challenges.
Team Leadership & Development
- Lead, mentor and manage teams of 20+ Data Analysts, Data Scientists, AI/MLOps professionals, Vision computing experts and AI platform engineers, fostering a culture of innovation, collaboration and technical excellence.
- Foster a collaborative and innovative culture that encourages experimentation, learning, and continuous improvement.
AI/ML Strategy & Execution
- Take complete responsibility of development and deployment of advanced machine learning models, predictive analytics and AI solutions for applications like forecasting, customer segmentation or process automation.
- Support organization with defining & executing the AI/ML strategy aligned with the company's business goals.
- Set up an SOP for shared leadership between Germany & BLR in identifying POC's that can be productionized.
- Stay current with emerging trends in AI/ML and guide the adoption of cutting-edge techniques.
- Oversee the end-to-end lifecycle of ML projects: problem framing, data preparation, model design, training, validation, deployment and monitoring.
- Ensure models are accurate, explainable, scalable and robust for production environments.
- Drive experimentation and adoption of cutting-edge AI/ML techniques (e.g., deep learning, NLP, computer vision, generative AI).
- Drive experimentation culture to evaluate and benchmark algorithms and solutions.
Business Development
- Identify and capitalize on growth opportunities using data-driven insights to shape market strategies, pitch solutions to clients and partners and secure new business.
- Recognize potential roles that can be transitioned from high-cost locations (HAM, MIA) to lower-cost locations (India).
- Establish a potential IT team, including full stack development and technical support roles (L2, L3).
- Position LTSI as a Center of Competence for Advanced Data Analytics and AI Solutions.
Cross-Functional Collaboration & Business Impact
- Collaborate with C-suite executives and department heads to ensure that analytics and data science initiatives align with organizational goals, promoting data-driven decision-making throughout the company.
- Identify potential digitization and automation opportunities with over 20 teams in LTSI and implement strategies to enhance quality and efficiency.
- Work with LTSI teams to develop digital skills and prepare a future-ready workforce.
- Initiate Digital Literacy programs for tailored for all LTSI employees.
- Partner with product, engineering and business stakeholders to identify opportunities for AI/ML applications.
Data Infrastructure & Governance
- Collaborate with global IT and engineering teams to ensure robust, scalable data pipelines, cloud infrastructure (AWS, Azure, GCP), and compliance with data governance and privacy regulations.
- Setup digital infrastructure to enable LTSI support functions to operate efficiently.
- Ensure data quality, integrity, and security while adhering to compliance standards.
- Collaborate with data engineering teams to build and optimize data pipelines.
- Ensure data governance ambassadors are in place.
- Promote best practices in MLOps, model monitoring and continuous improvement.
Performance Measurement
- Define KPIs and metrics to evaluate the impact of analytics and data science initiatives on business outcomes.
- Set annual cost saving targets and ensure these targets are met by implementing automation, strategic projects.
- Ensure the agreed upon KPI's and objectives are successfully achieved.
Stakeholder Communication
- Develop and distribute project charters, and clearly outline milestones.
- Optimize the utilization of budgeted FTEs.
- Communicate complex data insights, model outcomes, and strategic recommendations to non-technical stakeholders, including board members, clients, or partners.
Innovation & Trends
- Establish an innovation hub at LTSI to generate viable ideas and secure funding for their implementation.
- Stay ahead of industry trends in analytics, AI, and machine learning, integrating cutting-edge methodologies to maintain a competitive edge.
Key Skills
- Expertise in analytics tools (SQL, Tableau, Power BI) and data science frameworks (Python, R or Scala, TensorFlow, PyTorch, Scikit-learn, XGBoost, etc).
- Solid understanding of deep learning, NLP, computer vision, recommender systems or generative AI.
- Knowledge of big data technologies (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure).
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
- Experience with statistical analysis, machine learning and predictive modeling.
- Strong business acumen with experience in strategic planning and business development.
- Proven leadership and team management skills, with the ability to inspire and align diverse teams.
- Exceptional communication and presentation skills to influence stakeholders and articulate data-driven strategies.
- Knowledge of data engineering and data governance principles.
- Ability to bridge technical and business domains, translating complex data concepts into actionable Master's/PhD in Data Science, Statistics, Computer Science, Business Administration or a related field.
- 12+ years of experience in analytics and/or data science/ML roles, with 4+ years in a senior leadership role overseeing both functions.
- Proven track record of delivering AI/ML solutions in production.
- Proven track record of driving business impact through data initiatives and business development efforts.
- Experience in client-facing roles or strategic partnerships is a plus.
(ref:hirist.tech)
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