Data Scientist II

ACT Education•Iowa City, IA

1d•Remote

About The Position

ACT is seeking a Data Scientist II to help build a pathway navigation experience for high school students exploring and planning college and career options. The role designs, tests, and validates data models that power personalized college, career, and pathway recommendations. It combines student, education, assessment, interest, skills, career, and labor-market data to produce relevant, explainable, and actionable recommendations. The Data Scientist moves from early experimentation to production implementation while ensuring models are accurate, transparent, fair, and appropriate for students and families. The position works cross-functionally with product, engineering, research, design, and business partners. It involves developing and evaluating statistical, machine-learning, and generative AI models; designing product data collection and experiments; analyzing pilots and user studies; partnering on scalable cloud-based data pipelines; integrating complex data sources; and establishing processes for data security, documentation, deployment, monitoring, and maintenance. The role also develops personalization approaches that work with incomplete student data and makes model outputs understandable and actionable to stakeholders. This is a remote position based in the United States. It is a fixed-term position lasting six months, with the possibility of extension. Applicants must be authorized to work in the U.S. without visa sponsorship. The anticipated annual salary range is $95,000 to $105,000, with the finalist hired at a salary and level commensurate with qualifications.

Requirements

Bachelor’s degree in data science, computer science, statistics, applied mathematics, economics, operations research, or a related quantitative discipline, or an equivalent combination of education and experience.
At least 3 years of applied data science experience, including building statistical or machine-learning models and analyzing product experiments, pilots, or user studies.
Proficiency in Python and SQL.
Experience developing scalable data pipelines and moving data products or models from prototype toward production.
Ability to move from analysis and rapid prototyping to production implementation.
Knowledge of statistical modeling, machine learning, experimental design, and model evaluation.
Experience with scalable data pipelines, cloud environments, and integrating complex data sources.
Skill in using behavioral and performance data to develop personalized product experiences.
Knowledge of responsible AI, including fairness, bias mitigation, transparency, privacy, and appropriate use.
Ability to communicate across functions and translate research and business needs into actionable technical solutions.

Nice To Haves

Master’s degree in data science or a related field.
Experience with recommender systems, generative AI, natural language processing, or personalized and adaptive systems.
Experience with education, assessment, career, workforce, labor-market, occupational, or skills data.
Completion of the ACT National Career Readiness Certificate is recommended.

Responsibilities

Design, prototype, and evaluate models for personalized college, career, and pathway recommendations.
Apply statistical modeling, machine learning, and generative AI to test and improve product capabilities.
Evaluate model performance, fairness, bias, and appropriate use.
Design data collection strategies that capture student behavior, performance, and engagement while supporting product development.
Analyze pilot and user-study data and translate findings into product recommendations.
Define metrics and experiments to evaluate recommendation quality and product impact.
Partner with engineers to build scalable cloud-based data pipelines and move models from prototype into production.
Integrate student, education, assessment, skills, career, and labor-market data sources.
Establish reliable processes for data security, model documentation, deployment, monitoring, and maintenance.
Develop approaches that provide useful recommendations when student data is incomplete and adapt as new information becomes available.
Use behavioral and performance data to support personalized and adaptive product experiences.
Make model outputs understandable and actionable for stakeholders.
Collaborate across teams to align data, modeling, research, and product requirements.
Translate research and business needs into data collection plans and technical solutions.
Communicate analytical findings, assumptions, risks, and tradeoffs to technical and nontechnical stakeholders.