Lead Data Scientist building GenAI - driven products and ML solutions for S&P Global. Mentoring team members and delivering high - impact projects in a global environment.
About the role
- Data Scientist / ML Engineer at Franklin Templeton designing and productionizing machine learning systems for business solutions. Collaborating with teams to deliver scalable and reliable ML solutions.
Responsibilities
- Play a critical role in designing, building, and productionizing machine learning systems
- Focus on end-to-end ML lifecycle ownership, including data ingestion, feature engineering, model development, deployment, monitoring, and optimization in production environments
- Work closely with data engineering, platform, and product teams to deliver scalable, reliable, and secure ML solutions
- Design, implement, and maintain robust, scalable data pipelines for ML workloads
- Build automated data ingestion, validation, and preprocessing frameworks
- Collaborate with data engineers to integrate ML workflows into enterprise data platforms
- Optimize data storage and access patterns for high-volume, high-performance ML use cases
- Ensure data quality, lineage, and reproducibility across ML pipelines
- Develop, optimize, and maintain production-grade machine learning models
- Implement feature engineering pipelines and reusable ML components
- Design and build end-to-end ML architectures, from experimentation to deployment
- Apply model evaluation, testing, and validation frameworks to ensure robustness
- Lead efforts in Generative AI system design, mentoring team members on applied GenAI patterns and best practices
- Translate ambiguous business problems into clear technical designs and ML system architectures
- Deploy ML models using CI/CD pipelines, containerization, and cloud-native services
- Implement model monitoring, performance tracking, drift detection, and retraining strategies
- Partner with platform teams to ensure models meet security, scalability, and reliability standards
- Troubleshoot and optimize ML systems in production environments
- Contribute to ML platform standards, tooling, and reusable frameworks
- Work closely with product managers, engineers, and business stakeholders to define technical requirements
- Translate analytical insights into engineering deliverables for downstream systems
- Communicate technical designs, trade-offs, and system behavior to both technical and non-technical audiences
- Collaborate with domain experts to integrate business logic into ML system design
- Stay current with advancements in ML engineering, cloud platforms, MLOps, and Generative AI
- Prototype and evaluate new tools, architectures, and frameworks
- Contribute to technical documentation, design reviews, and best practices
- Continuously improve system reliability, performance, and maintainability.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related discipline
- 5+ years of hands-on experience building and deploying ML systems in production
- Strong proficiency in Python with experience building production ML code
- Advanced SQL skills and experience working with large-scale datasets
- Experience with machine learning frameworks
- Hands-on experience with data pipelines, feature stores, and ML workflows
- Familiarity with Generative AI models and applied GenAI system patterns
- Experience deploying models using containers (Docker) and CI/CD pipelines
- Exposure to cloud platforms (AWS, Azure, or GCP) and managed ML services
- Understanding of model monitoring, drift detection, and lifecycle management
- Strong ability to translate business problems into engineering solutions
- Comfortable working with ambiguous requirements and defining technical direction
- Experience designing modular, reusable, and maintainable systems
- Strong debugging, performance optimization, and problem-solving skills
- Ability to explain complex ML systems and trade-offs to diverse stakeholders
- Strong written and verbal communication skills
- Team-oriented with the ability to work independently and take ownership
- Effective planning, prioritization, and execution in fast-paced environments.
Benefits
- Three weeks paid time off the first year
- Medical, dental and vision insurance
- 401(k) Retirement Plan with 85% company match on your pre-tax and/or Roth contributions, up to the IRS limits
- Employee Stock Investment Program
- Tuition Assistance Program
- Purchase of company funds with no sales charge
- Onsite fitness center and recreation center*
- Onsite cafeteria*
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