Data Scientist I

CareSource•Dayton, OH

1d•$72,200 - $115,500

About The Position

The Data Scientist I supports the design and validation of predictive models, machine learning models, and artificial intelligence to inform and improve business processes across operational, clinical, financial, marketing, and other areas. The role assists with analytical planning, mining and analyzing structured and unstructured data, developing algorithms, and creating visualizations to demonstrate algorithm performance. It also contributes to statistical validation of clinical program and intervention outcomes under guidance, collaborates with other teams to integrate solutions, communicates findings to stakeholders, and helps prepare code and model pipelines for operational use. The position also involves researching emerging technologies and performing other job-related duties as requested. The role requires a bachelor's degree in data science, mathematics, statistics, engineering, computer science, or a related field, although equivalent relevant work experience may be accepted in lieu of education. Candidates should be familiar with SQL and Python or R, statistical methods, predictive modeling and machine learning, natural language processing, feature engineering, exploratory data analysis, optimization, and artificial intelligence. Familiarity with cloud services and modern data stacks is preferred; beginner-level knowledge of reporting or dashboards in Power BI or another business intelligence application is preferred. This role is performed in a general office environment and may require sitting or standing for extended periods. Travel is not typically required. The compensation range is $72,200.00 to $115,500.00, with potential eligibility for a bonus tied to company and individual performance. CareSource also describes a substantial and comprehensive total rewards package.

Requirements

Bachelor's degree in data science, mathematics, statistics, engineering, computer science, or another related field; equivalent years of relevant work experience may be accepted in lieu of the required education.
Familiarity with SQL and at least one of Python or R.
Ability to understand basic statistical analyses and techniques, including A/B testing, general significance testing, and sampling methodologies.
Working knowledge of predictive modeling and machine learning algorithms, including generalized linear models, non-linear supervised learning models, clustering, decision trees, and dimensionality reduction.
Working knowledge of unsupervised and deep learning methodologies, including clustering, neural networks, and transformers.
Working knowledge of natural language processing technologies, including optical character recognition, named entity recognition, visual learning models, and large language models.
Working knowledge of NLP-specific feature extraction techniques, including tokenization, embeddings, and text-based transformations.
Familiarity with feature engineering techniques and exploratory data analysis.
Familiarity with optimization techniques and artificial intelligence methods.
Ability to mine and analyze large quantities of structured and unstructured data and identify patterns, irregularities, and deficiencies.
Proficiency with Microsoft Office, including Excel, PowerPoint, Word, and Access.
Demonstrated critical thinking, verbal communication, presentation, and written communication skills.
Ability to work independently and within a cross-functional team environment.

Nice To Haves

Experience with cloud services such as Azure, AWS, or GCP and modern data stacks such as Databricks or Snowflake.
Beginner-level knowledge of developing reports or dashboards in Power BI or other business intelligence applications.

Responsibilities

Support the creation, maintenance, and communication of analytical plans for data science projects.
Assist in mining and analyzing large structured and unstructured datasets.
Use a wide range of data sources to develop algorithms for predicting risk, understanding drivers, and detecting outliers.
Support development of visualizations that demonstrate the efficacy of developed algorithms.
Contribute, under guidance, to statistical validation and analysis of outcomes associated with clinical programs and interventions.
Collaborate with other teams to integrate solutions with existing systems.
Communicate results and ideas to key stakeholders.
Assist in preparing code for operationalizing end-to-end model pipelines and deliverables for business consumption.
Research and stay up to date on emerging technologies.
Perform other job-related duties as requested.