Data Scientist III

Job Summary

This individual contributor is primarily responsible for participating in the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats under the guidance of more senior data scientists. This role is also responsible for developing detailed problem statements outlining hypotheses and their effect on target clients/customers, analyzing and investigating data sets and summarizing key characteristics, selecting, manipulating and transforming data into features used in machine learning algorithms, training statistical models under the guidance of more senior data scientists, deploying and maintaining reliable and efficient models through production, verifying model performance, and working with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Essential Responsibilities

• Pursues effective relationships with others by proactively providing resources, information, advice, and expertise with coworkers and members. Listens to, seeks, and addresses performance feedback; provides mentoring to team members. Pursues self-development; creates plans and takes action to capitalize on strengths and develop weaknesses; influences others through technical explanations and examples. Adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work; helps others adapt to new tasks and processes. Support and responds to the needs of others to support a business outcome.
• Completes work assignments autonomously by applying up-to-date expertise in subject area to generate creative solutions; ensures all procedures and policies are followed; leverages an understanding of data and resources to support projects or initiatives. Collaborates cross-functionally to solve business problems; escalates issues or risks as appropriate; communicates progress and information. Supports, identifies, and monitors priorities, deadlines, and expectations. Identifies, speaks up, and implements ways to address improvement opportunities for team.
• Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics.
• Participates in the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats under the guidance of more senior data scientists by transforming, cleansing, and storing data for consumption by downstream processes; writing and optimizing diverse SQL queries; and demonstrating a working knowledge of database fundamentals.
• Analyzes and investigates data sets and summarizes key characteristics by employing data visualization methods; and determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions.
• Selects, manipulates, and transforms data into features used in machine learning algorithms by leveraging techniques to conduct dimensionality reduction, feature importance, and feature selection.
• Trains statistical models under the guidance of more senior data scientists by using algorithms and data mining techniques; testing models with various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.
• Deploys and maintains reliable and efficient models through production.
• Verifies model performance by demonstrating a working knowledge of a variety of model validation techniques to assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen model performance.
• Works with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by delivering insights and values from heterogeneous data to investigate problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical audiences.

Job Qualifications

Minimum Qualifications

• Minimum two (2) years’ experience working with Exploratory Data Analysis (EDA) and visualization methods.
• Minimum one (1) year machine learning and/or algorithmic experience.
• Minimum two (2) years’ statistical analysis and modeling experience. Minimum two (2) years programming experience.
• Bachelor’s degree in mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum three (3) years’ experience in data science or a directly related field. Additional equivalent work experience in a directly related field may be substituted for the degree requirement. Advanced degrees may be substituted for the work experience requirements.

Additional Requirements

• Advanced Quantitative Data Modeling; Applied Data Analysis; Data Extraction; Data Visualization Tools; Machine Learning; Relational Database Management; Microsoft Excel; Design Thinking; Business Intelligence Tools; Data Manipulation/Wrangling; Data Ensemble Techniques; Feature Analysis/Engineering; Open-Source Languages & Tools; Model Optimization; Algorithms

Preferred Qualifications

• One (1) year experience serving in technical leadership, project leadership, mentoring, or lead analyst/data scientist role, with or without direct reports.
• Master’s degree in data science, Statistics, Computer Science, Engineering, Mathematics, Economics, Public Health, Healthcare Informatics, or a related quantitative field.
• One (1) year experience delivering technical, analytical, and business presentations to management, executive leadership, or non-technical audiences.
• One (1) year experience working in a complex matrixed organization involving cross-functional business, operational, and technical stakeholders.
• One (1) year experience supporting regulatory, compliance, accreditation, audit, quality, cybersecurity, or healthcare reporting initiatives.
• Two (2) years’ experience developing dashboards and visual analytics solutions using Tableau or Power BI.
• Two (2) years’ experience working with Databricks for data engineering, analytics, machine learning, or enterprise-scale data processing.
• Two (2) years’ experience working with Oracle, SQL Server, Snowflake, or other enterprise relational databases.
• Two (2) years’ experience using Python for data science, machine learning, statistical analysis, automation, or data engineering.
• Two (2) years’ experience developing and deploying machine learning, predictive analytics, forecasting, or optimization models in a production environment.
• Two (2) years’ experience working with cloud-based data platforms, data lakes, or modern analytics architectures.
• Two (2) years’ experience applying data governance, data quality, metadata management, or master data management principles.
• Two (2) years’ experience building and maintaining scalable data pipelines, semantic models, and analytical data products.
• Two (2) years’ experience leveraging AI/ML frameworks, open-source analytics tools, or statistical programming languages to solve complex business problems.
• Experience supporting asset management, lifecycle planning, workforce analytics, operational performance management, cybersecurity analytics, or service delivery analytics is highly desirable.
• Tableau Certified Data Analyst, Tableau Desktop Specialist, Microsoft Power BI Data Analyst Associate, Databricks Certified Data Analyst Associate, Azure Data Scientist Associate, or similar professional certification preferred.