Senior Manager, Data Science

Job Description:

Position Description:

Extracts, processes, analyzes, and manages big and small data, using SQL, Python, and R. Gathers internal and external data (structured or unstructured) from sources using programming languages -- Python, SAS, SQL, and VBA. Applies data science and advanced analytics to products and services to support customer needs, improve customer experiences, develop new products, and support marketing, sales, and technology initiatives. Manages multi-terabyte quantities of data using Cloud-based data science platforms -- Snowflake, Sagemaker, Hadoop, and MapReduce. Formulates mathematical or simulation models of problems -- relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters. Uses business knowledge to perform project management for technology projects. Acts as a primary liaison for business units to resolve various project/technology issues. Establishes full project life cycle plans for one or more complex projects.

Primary Responsibilities:

• Establishes best practices in the implementation of data pipelines, model workflow and reporting by consolidating resources and working across functional teams.
• Facilitates agile team ceremonies for story planning.

• Accountable for integration into larger, multi-disciplined projects.
• Elicits, captures, and interprets customer problems from multiple perspectives across multiple projects or within a program.
• Ensures quality and timely requirements into workable project documents process enhancements.
• Contributes to estimation, planning, analysis, design, and development of multiple complex projects.
• Processes and analyzes large, complex, and unstructured data.

• Develops and executes Machine Learning (ML) and Artificial Intelligence (AI) approaches to data analysis.
• Establishes and project manages analysis plans for multiple complex work streams.
• Holds accountability for integration into larger, multi-disciplined projects.
• Independently researches and tests new tools and technologies for building, testing, and monitoring AI models.
• Generates new insights by staying a breast of publications, tools, and techniques.

• Performs exploratory data analysis, unstructured data mining, and predictive and prescriptive analytics.
• Performs data cleansing, preparation and annotation, feature engineering, exploratory data analysis, model evaluation and selection, and ML pipeline design and development.
• Implements best practices for predictive and prescriptive analytics and conducting model evaluations and code reviews. Interprets results and draws key business insights from advanced quantitative analyses and presents findings to broader audiences and senior management.
• Defines data requirements, gathers and validates information, and applies judgment and statistical tests.
• Designs, conducts, and evaluates experimental operational models in cases where models cannot be developed from existing data.

• Mentors junior team members in data sources, tools, and technologies.
• Performs validation and tests models to ensure adequacy and reformulates models as necessary.
• Collaborates with senior management to identify and solve operational problems and to clarifymanagement objectives.
• Collaborates with business stakeholders to identify and prioritize analytical needs, define requirements, estimate project scopes, and design and plan for complex AI work streams.
• Prepares management reports and provides clear and concise communication when reporting on project topic or project status.

Education and Experience:

Bachelor's degree (or foreign education equivalent) in Computer Science, Data Science, Analytics, Operations Research, Economics, or a closely related field and five (5) years of experience in the job offered or five (5) years of experience 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 (or foreign education equivalent) in Computer Science, Data Science, Analytics, Operations Research, and Economics, or a closely related field and two (2) years of experience in the job offered or two (2) years of experience designing and building complex and scalable Artificial intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise ("DE") developing supervised and unsupervised Machine Learning (ML) algorithms -- regression, decisions 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 structures across Cloud and on-premise data warehouses -- Oracle, Greenplum/Postgres, Hadoop/Hive, Snowflake, and Redis -- using SQL or HQL; optimizing complex queries using database techniques -- partitioning and parallel processing; aggregating time series and transaction tables; analyzing appropriate features for modeling; and preventing data leakage through the analysis of relationships between potential inputs and outputs.
• DE analyzing technology solutions for supporting model deployment and integration in Cloud and on-premise environments; and building model deployment and integration workflows, using Amazon Web Services (AWS), on-premise Hadoop, and UNIX platforms through Python scripts, cron jobs, Docker images, and APIs.
• DE migrating existing processes from on-premise environments to AWS platforms, using Extract-Transform-Load (ETL) procedures, Python, and Docker containers; 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.

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Certifications:

Category:

Data Analytics and Insights