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.

About the Department

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.

Role Overview

As a Data Scientist/ML Engineer, you will design, build, and productionize 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.

Responsibilities

• 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.

Qualifications Education & Experience

• 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.

Core Technical Skills

• 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.

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 requirements and 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

Base salary range: $125,000 to $160,000. Additional compensation includes discretionary bonus, 401(k) plan, health insurance, and other perks. Compensation decisions consider factors such as location, job-related knowledge, skills, and experience.

When applying, attach your resume. Applications without a resume file attachment will not be reviewed.
Hybrid
Benefits

• Three weeks paid time off in the first year.
• Medical, dental, and vision insurance.
• 401(k) Retirement Plan with 85% company match on pre-tax and/or Roth contributions up to IRS limits.
• Employee Stock Investment Program.
• Tuition Assistance Program.
• Purchase of company funds with no sales charge.
• Onsite fitness center and recreation center (applicable at certain locations).
• Onsite cafeteria (applicable at certain locations).

*Only applicable at certain locations

Equal Opportunity Employment

Franklin Templeton is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all applicants and employees, and we evaluate qualified applicants without regard to ancestry, age, color, disability, genetic information, gender, gender identity, or gender expression, marital status, medical condition, military or veteran status, national origin, race, religion, sex, sexual orientation, and any other basis protected by federal, state, or local law, ordinance, or regulation. If you need an accommodation or adjustment to search for or apply for a position, please send an email to (see below) In your email, include the accommodation or adjustment you are requesting, the job title, and the job number you are applying for.