Research Data Scientist

At Jack Henry, we’re more than a technology company, we’re a force for good in financial services. We’re redefining how community banks and credit unions connect with the people they serve. Our mission is rooted in people inspired innovation, empowering financial institutions to deliver seamless, secure, and human centered experiences. We deliver cutting-edge solutions that are paving the way for the next generation of digital banking and payments, but our true impact begins with our associates. If you're ready to help transform an industry and grow with a company that values purpose, collaboration, and excellence then we’d love to meet you.

We're hiring a Research Data Scientist who trains, tests and evaluates analytic and machine learning solutions that directly support decisions. You'll partner closely with business, data, and engineering teams to turn complex, ambiguous problems into reproducible models, experiments, and analytic decisions grounded in statistics and modern ML practices.

Our ideal candidate brings genuine depth in both statistics and machine learning. You're fluent in the full ML lifecycle - from problem framing, designing training and validation pipelines, selecting and tuning models, and evaluating them against both technical metrics and business outcomes. You reach naturally for hypothesis testing, confidence intervals, and experimental design when the situation calls for them, and you know the difference between statistical significance and practical significance. Time series and forecasting are familiar ground: you understand stationarity, seasonality, and temporal leakage, and you build forecasting pipelines that hold up in production. You pair all of this with clear, plain-language communication.

This position will work in a hybrid schedule of at least 3 days per week from either our Allen, TX or Charlotte, NC office. All positions, regardless of location, may require an onsite interview or in-person onboarding requirement to verify your identity.

This position is ineligible for immigration sponsorship and support. Please do not apply if at any time you will need immigration support now or in the future (i.e., H-1B, PERM).

The salary range for this position is $80,350- $100,000 and will be determined based on location and experience level.

What you’ll be responsible for:

• Applies knowledge of statistics, machine learning, programming and advanced mathematics to recognize patterns, identify opportunities, and drive decisions with data.
• Works with engineers to translate data science models into new products, services, and features. You will work across organizations and teams internal consulting specialists on analytic challenges.
• Communicates findings to both business and IT teams in plain language to influence how the organization approaches analytic challenges.
• Explores and examines data from multiple disparate sources; reviews and analyzes all incoming data with the goal of discovering previously hidden insights that can provide a competitive advantage or address a pressing business problem
• Performs other duties as assigned

What you’ll need to have:

• Minimum of 4 years of professional data science experience, or a recently completed Master's or PhD in Data Science, Statistics, Computer Science, or a related field, which may be considered in lieu of professional experience.
• Experience with Python and SQL: Python for modeling, scripting, and tooling beyond notebooks; SQL for non-trivial queries including joins, aggregations, and window functions.
• Experience with Statistics and inference: hypothesis testing, confidence intervals, regression, and experimental or quasi-experimental design — applied with rigor, not just familiarity.
• Experience with ML training, evaluation, and explainability: end-to-end pipelines, cross-validation, hyperparameter tuning, calibration, evaluation beyond a single metric, and XAI techniques (e.g., SHAP, LIME) where trust or accountability matter.

What would be nice for you to have:

• Deep learning: Hands-on experience with PyTorch or Keras/TensorFlow — training, debugging, and evaluation beyond tutorial-level notebooks
• Experience with Time series and forecasting: stationarity, seasonality, leakage-safe temporal validation, and evaluation appropriate to the forecasting horizon.
• MLOps: Familiarity with experiment tracking, model registries, and basic deployment and monitoring concepts (drift detection, rollback, latency/throughput)
• Broader ML domain experience: NLP, reinforcement learning, or tabular deep learning applied to real problems beyond toy examples
• AI agents and LLM-based systems: Exposure to tool-using agents, RAG, or workflow automation with a focus on measurable impact, reliability, and responsible data use
• Cloud and large-scale data: Experience running training jobs or working with managed ML/compute services; comfort with messy, production-grade data at scale

If you got this far, we hope you're feeling ex