Software Gigs

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace. Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.

As a Data Developer, you will design, develop, and maintain scalable ETL/ELT pipelines and cloud-based data platforms that integrate enterprise systems and support AI/ML initiatives for the U.S. Navy. You will perform data engineering tasks including cleansing, transformation, and optimization of SQL and Apache Spark workloads on AWS, while also developing predictive models and generative AI solutions using Python-based technologies. You will build interactive dashboards and visualizations in Tableau or Qlik, translating technical findings into actionable insights for stakeholders. An active Secret clearance, U.S. citizenship, and a bachelor's degree in a technical field (or 4+ years of relevant professional experience) are required.

As a client-facing data analyst, you'll work with Department of the Navy clients to understand their data needs, transform complex structured and unstructured data into actionable insights, and advise on how findings impact DoD acquisition programs and IT modernization. You'll use tools like Tableau, Python, R, or SQL for data manipulation; Databricks or Spark for data engineering; and Power Apps/Power Automate for automation. You must hold or be able to obtain a Secret clearance and have 4+ years of DoD client experience.

Senior individual contributor responsible for end-to-end demand forecasting across Intuit's Global Business Solutions Group portfolio. Build and maintain predictive models incorporating time-series analysis, regression, causal inference, and ML techniques to translate business signals into accurate, decision-ready demand forecasts at multiple granularities. Conduct A/B/n experiments to validate modeling choices, quantify forecast risk through scenario analysis, and act as trusted partner between technical teams and business stakeholders (Workforce Management, Capacity Planning, Finance, Marketing, Product) to ensure forecasts drive downstream capacity and expense planning. Continuously advance forecasting methodology, evaluate where AI/ML meaningfully improves accuracy, and support real-time in-season re-forecasting and analytics.