Senior Manager, Data Science

Fidelity Investments•Boston, MA

20h•Hybrid

About The Position

Position Description: Designs and implements Machine Learning (ML) and Deep Learning (DL) algorithm approaches in multiple projects. Programs Machine Learning frameworks using Python and R. Conducts exploratory data analysis according to measurements, unstructured data analysis, predictive analytics, and prescriptive analytics using Big Data, Natural Language Processing (NLP), and chatbot technologies (Elasticsearch and Solr). Moves multiple high priority projects to on-time and high-quality delivery. Communicates data-derived insights to influence business actions and strategy, including predictive analytics. Manages multiple projects and programs, and mentors and coaches other analysts. Collaborates with Senior Management to refine requirements and propose new avenues of research. Primary Responsibilities: Oversees the planning, design, and execution of multiple ML-based projects. Enables learning, collaboration, and mentoring of software engineers and data scientists. Performs ML lifecycle management and de-bugging for bias, drift, and fragility. Adheres to engineering and data science best-practices (Git, documentation, and test automation). Implements emerging technologies into screening programs. Creates business and technical requirements. Works across teams and influences the direction of external teams. Delivers oral or written presentations of the results of mathematical models and data analysis to management or other end users. Analyzes, manipulates, or processes large sets of data using statistical software. Sets a strategic direction for data identification, collection, and qualification activities. Leads data analysis for multiple projects with diverse scope and complex business and technical challenges across several business units and functions. Coordinates and guides data science and data engineering elements of AI projects and ML techniques. Implements new technologies in a production environment with product, IT, and data engineering teams.

Requirements

Bachelor’s degree in Analytics, Statistical Modeling, Economics, Computer Science, Engineering, Information Technology, Information Systems or a closely related field (or foreign education equivalent) and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.
Or, alternatively, Master’s degree in Analytics, Statistical Modeling, Economics, Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and two (2) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.
Or, alternatively, PhD degree in Analytics, Statistical Modeling, Economics, Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and no experience.
Demonstrated Expertise (“DE”) performing predictive modeling to develop, train, and evaluate supervised and unsupervised ML algorithms, including Regression, Clustering, Decision Trees, Gradient Boosting, and Neural Networks; and performing Feature Selection from data in s3 buckets and any SQL and PostGRSQL compatible databases; and implementing Hyper Parameter tuning, using Python, Machine and Deep Learning frameworks (scikit-learn, TensorFlow, or PyTorch).
DE designing and developing NLP models for embeddings, text classification, information retrieval, and summarization, using Python and NLP frameworks including BERT, spaCy, and Hugging Face; and processing unstructured text data, extracting relevant information, and generating actionable insights to support internal business decision-making.
DE analyzing financial and macroeconomic data to extract insights, identify trends, and support decision making; researching relationships between economic indicators, market conditions, and relevant financial metrics aligned with strategic initiatives of finance using statistical and econometrics methods including correlation analysis, regression, granger causality tests, and cointegration tests; and implementing monitoring solutions to detect model drift, concept drift, and model value accumulation, demonstrating long-term model stability using algorithms from scikit-learn including Kolmogorov-Smirnov, Anderson-Darling, principal component analysis, and Nelson rules.
DE creating model predictions using SHAP and counterfactual forecasting; communicating the results of model testing and explanatory methods to senior leadership, business stakeholders, and subject matter experts, in support of Federal Reserve Regulation SR 11-7; engaging with business stakeholders to define key metrics, aligning on forecasting expectations, and ensuring model outputs support business objectives and risk management pursuant to SR 11-7 ongoing monitoring requirements; and presenting those monitoring results through data visualizations, storytelling, and executive reports, using PowerBI, Plotly, and Matplotlib.

Responsibilities

Designs and implements Machine Learning (ML) and Deep Learning (DL) algorithm approaches in multiple projects.
Programs Machine Learning frameworks using Python and R.
Conducts exploratory data analysis according to measurements, unstructured data analysis, predictive analytics, and prescriptive analytics using Big Data, Natural Language Processing (NLP), and chatbot technologies (Elasticsearch and Solr).
Moves multiple high priority projects to on-time and high-quality delivery.
Communicates data-derived insights to influence business actions and strategy, including predictive analytics.
Manages multiple projects and programs, and mentors and coaches other analysts.
Collaborates with Senior Management to refine requirements and propose new avenues of research.
Oversees the planning, design, and execution of multiple ML-based projects.
Enables learning, collaboration, and mentoring of software engineers and data scientists.
Performs ML lifecycle management and de-bugging for bias, drift, and fragility.
Adheres to engineering and data science best-practices (Git, documentation, and test automation).
Implements emerging technologies into screening programs.
Creates business and technical requirements.
Works across teams and influences the direction of external teams.
Delivers oral or written presentations of the results of mathematical models and data analysis to management or other end users.
Analyzes, manipulates, or processes large sets of data using statistical software.
Sets a strategic direction for data identification, collection, and qualification activities.
Leads data analysis for multiple projects with diverse scope and complex business and technical challenges across several business units and functions.
Coordinates and guides data science and data engineering elements of AI projects and ML techniques.
Implements new technologies in a production environment with product, IT, and data engineering teams.