At Franklin Templeton were advancing our industry forward by developing new and innovative ways to help our clients achieve their investment goals. Our dynamic firm spans asset management wealth management and fintech offering many ways to help investors make progress toward their goals. Our talented teams working around the globe bring expertise thats both broad and unique. From our welcoming inclusive and flexible culture to our global and diverse business we provide opportunities to help you reach your potential while helping our clients reach theirs.

Come join us in delivering better outcomes for our clients around the world!

About the Department

Franklin Templeton Technology (FTT) drives the technology strategy and delivers innovative technology solutions for Franklin Templeton (FT) a global investment leader delivering innovative multi-asset solutions across public and private markets. FT integrates asset allocation manager research and implementation to drive portfolio construction execution and strategic oversight. Joining FT means working in a collaborative growth-oriented environment that values innovation in investment technology.

What is the Data Scientist (ML Engineer) in the FTT Digital Technology Group Responsible For

As a Data Scientist / ML Engineer you will play a critical role in designing building and productionizing machine learning systemsthat 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 teamsto deliver scalable reliable and secure ML solutions. Under the guidance of senior technical leaders you will contribute to engineering-grade ML architecture gain hands-on experience with cloud-native ML systems and help advance the organizations AI capabilities.

Ongoing Responsibilities (Engineering-Focused)

Data Engineering & Pipelines

• Design implement and maintain robust scalable data pipelinesfor 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.

Machine Learning Engineering

• Develop optimize and maintain production-grade machine learning models.

• Implement feature engineering pipelinesand reusable ML components.

• Design and build end-to-end ML architectures from experimentation to deployment.

• Apply model evaluation testing and validation frameworksto 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.

MLOps & Production Systems

• 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.

Cross-Functional Engineering Collaboration

• Work closely with product managers engineers and business stakeholdersto define technical requirements.

• Translate analytical insights into engineering deliverablesfor 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.

Continuous Learning & Technical Innovation

• 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.

Ideal Qualifications Skills & Experience (Engineering-Heavy)

Education & Experience

• Bachelors or Masters degree in Computer Science Engineering Data Science or related discipline.

• 5 years of hands-on experiencebuilding and deploying ML systems in production.

Core Technical Skills

• Strong proficiency in Pythonwith experience building production ML code.

• Advanced SQLskills 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.

ML Engineering & MLOps

• 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.

Engineering Mindset

• Strong ability to translate business problems into engineering solutions.

• Comfortable working with ambiguous requirementsand defining technical direction.

• Experience designing modular reusable and maintainable systems.

• Strong debugging performance optimization and problem-solving skills.

Collaboration & Communication

• 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.

Compensation Range: Along with base compensation other compensation is offered such as a discretionary bonus 401k plan health insurance and other perks. There are several factors taken into consideration in making compensation decisions including but not limited to location job-related knowledge skills and experience. At Franklin Templeton we apply a total reward philosophy where all aspects of compensation and benefits are taken into consideration in determining compensation. The salary benefits and variable rewards will reflect the seniority of the position and a competitive market rate. We expect the annual salary for this position to range between $125000 to $160000.

When applying please be sure to attach your resume / CV. Applications without a resume file attachment will not be reviewed.

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Experience our welcoming culture and reach your professional and personal potential!