Director, Data Science

Fidelity Investments•Durham, NC

18h•Hybrid

About The Position

Position Description: Leads and oversees end-to-end data science initiatives, guiding teams through data cleansing, preparation, annotation, feature engineering, exploratory analysis, and model development. Provides strategic direction on Machine Learning (ML) pipeline architecture, ensures alignment with business objectives, and drives cross-functional collaboration to deliver scalable, high-impact solutions. Draws on in-depth knowledge of the business or function to provide business unit-wide solutions by building, testing and monitoring AI models. Researches and recommends new technologies, and seizes opportunities by staying abreast of publications, tools, and techniques from the global Artificial Intelligence (AI/ML) community, in support of the strategic direction of the business unit and to achieve business-unit-wide solutions.

Requirements

Bachelor’s degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Or, alternatively, Master’s degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Demonstrated Expertise (“DE”) developing supervised and unsupervised Machine Learning (ML) algorithms -- regression, gradient boosting trees/random forest, neural network, feature selection/reduction, clustering, and parameter tuning -- using R, Python, and SAS programming languages; and analyzing and evaluating model results by creating data visualizations and business intelligence reports in Tableau and Adobe Analytics.
DE performing data wrangling and feature engineering for large, complex data across Cloud and on-premise data warehouses -- Oracle, Greenplum/Postgres, Hadoop/Hive, Snowflake, S3, and Redis -- using SQL, Python, and database specific SQL; standardizing and optimizing complex queries using database techniques -- partitioning and parallel processing; aggregating time series and transaction tables; creating appropriate features for modeling out of structured and unstructured data; detecting and preventing data leakage and model biases through model fairness measures using open-source AI fairness and ethics libraries.
DE analyzing technology solutions for supporting model deployment and integration in Cloud and on premise environments; and building model deployment and integration workflows on Amazon Web Services (AWS), on-premise Hadoop, and UNIX platforms through Git, Jenkins, Python scripts, cron jobs, step functions, Docker images, and APIs.
DE migrating existing AI/ML processes from on-premise environments to AWS platforms, using Extract- Transform-Load (ETL) procedures, Python, and Docker containers; creating data quality guardrails to validate model inputs and outputs using ICEDQ; and addressing financial services Cloud security constraints and record systems for workplace services -- 401(K), defined benefits, and workplace compensation and retirement plans, using AWS security tools.

Responsibilities

Identifies business opportunities and evaluates best approaches for predictive or prescriptive analytics.
Implements best practices for model development, iteration, as well as code management and conducts code reviews.
Draws key business insights from advanced quantitative analyses and presents findings to broader audience.
Leads the design and deployment of advanced analytics solutions that convert raw data into actionable intelligence.
Delivers scalable insights, while aligning analytics infrastructure with business priorities.
Directs the development and integration of analytics frameworks that transform raw data into strategic insights.
Ensures solutions are scalable, business-aligned, and drive data-informed decision-making across the organization.
Leads and oversees the full AI/ML lifecycle -- data ingestion, model development, training, deployment, and monitoring.
Identifies and consults with internal and external technical resources to produce cross-company strategic designs.
Consults on deployment of major project deliverables.
Initiates and drives project or strategy discussions with users or external groups to resolve issues.
Sets vision, goals, and direction of team/organization.
Plans and leads organization-wide initiatives.
Provides leadership, technical supervision, and expertise to multiple teams in broad technical areas on complex organization-wide projects.
Advises senior management on technical strategy.
Regularly provides guidance, training, and coaching to other team members for performance and career development.
Identifies and plans for future resource needs.