Data Scientist III

Duties: Demonstrates up-to-date expertise and applies this to the development, execution, and improvement of action plans by providing expert advice and guidance to others in the application of information and best practices; supporting and aligning efforts to meet customer and business needs; and building commitment for perspectives and rationales. Provides and supports the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities. Leads small and participates in large data analytics project teams by serving as a technical lead for analytics projects; working with project teams and business partners to determine project goals; developing contingency plans for data analysis; determining modeling based on business needs; directing the analysis of data; gathering data and developing reports as needed; utilizing business knowledge to ensure data supports project goals; analyzing data based on identified variables; reviewing data results to ensure accuracy; and communicating results and insights to the project team and business partners. Presents data insights and recommendations to key stakeholders by developing insights based on data analysis; applying analytical results to project goals; identifying trends and key insights; translating results into business actions; and presenting insights and recommendations to key stakeholders. Participates in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; building and maintaining a library of reusable algorithms for future use; ensuring developed code is documented; and coaching and mentoring analysts across the division and project teams. Develops analytical models to drive analytics insights by gathering data from internal and external sources; evaluating data usability based on project goals; synthesizing data into large datasets to support project goals; developing statistical models and computational algorithms to analyze data; utilizing the analytics project lifecycle process to drive predictive modeling; coding, testing, and maintaining analytical software tools; identifying trends, patterns and discrepancies in data; training statistical models for replication for future projects; and presenting data insights and recommendations to key stakeholders.

Minimum education and experience required: Master’s degree or the equivalent in Statistics, Analytics or related field; OR Bachelor’s degree or the equivalent in Statistics, Analytics, or related field plus 2 years of experience in data science or related area. Skills Required: Must have experience with: Coding and developing data analysis and machine learning solutions using Python; Querying, joining and aggregating data using SQL relational databases (MySQL, PostgreSQL); Designing and implementing predictive machine learning and deep learning models (logistic regression, decision trees, random forest, gradient boosting, and neural networks) using frameworks Pytorch & Tensorflow; Training and evaluating unsupervised learning models: clustering, topic modeling & dimensionality reduction; Performing data processing, cleansing, feature engineering, and transformation of structured datasets using Pandas & Numpy; Developing scalable data processing pipelines using PySpark & Spark SQL on distributed data processing platforms; Big data analytics platforms: Hive & Databricks; Developing and deploying natural language processing (NLP) models using transformer-based models; Applying statistical analysis including hypothesis testing, distributions, confidence intervals, and model evaluation metrics including precision, recall & F1-score; Designing and implementing Generative AI solutions using RAG, LLMs and Prompting techniques; Deploying machine learning models using Docker containers & Kubernetes; Developing and executing machine learning workflows on cloud platforms AWS & Azure; Implementing time series and anomaly detection models using Python & SQL; AI and model governance practices, including validation, monitoring & bias assessment. Employer will accept any amount of graduate coursework, graduate research experience or professional experience with the required skills.