Data Scientist; ML Engineer

Company Overview

At Franklin Templeton, we’re advancing industry forward by developing innovative ways to help clients achieve investment goals. Our dynamic firm spans asset management, wealth management, and fintech, offering many opportunities for global teams to help investors progress toward their goals while fostering an inclusive, flexible culture.

Franklin Templeton Technology (FTT) drives technology strategy and delivers innovative solutions across public and private markets. FTT integrates asset allocation, research, and implementation to support portfolio construction, execution, and strategic oversight. FTT values collaboration, growth, and innovation in investment technology.

As a Data Scientist / ML Engineer
, you will design, build, and product ionize machine learning systems that solve real-world business problems. You will focus on end-to-end ML lifecycle ownership, including data ingestion, feature engineering, model development, deployment, monitoring, and optimization in production environments. You will 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 and mentor 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.

• 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.
• Ability to design scalable, fault‑tolerant ML architectures.

• Strong ability to translate business problems into engineering…