Own the modeling and statistical lifecycle for enterprise AI initiatives. Build predictive models for anomaly detection, workflow forecasting, and risk mitigation; analyze and wrangle large-scale financial data; validate accuracy and bias; ensure regulatory compliance; create training datasets; establish model performance KPIs; and communicate quantitative findings to business and technology stakeholders.
Position Summary
The AI Data Scientist will own the modeling and statistical lifecycle of enterprise AI initiatives. The role will partner with business operations to structure financial data, build predictive models, and ensure AI investments deliver high-accuracy insights that reduce risk and improve decision-making. The role requires a rigorous quantitative expert who can balance complex statistical analysis with strict financial regulatory standards and drive alignment across business and technology teams.
Key Responsibilities- Develop and train advanced predictive models for anomaly detection, workflow forecasting, and risk mitigation
- Perform deep statistical analysis and data wrangling within large-scale enterprise environments
- Validate model accuracy rigorously, test for bias, and ensure all outputs meet strict financial compliance standards
- Partner with business leaders to understand the nuance of financial data and transform raw inputs into training datasets
- Establish KPIs tied to model accuracy, prediction precision, and false-positive reduction
- 5+ years of experience building and validating predictive models in a corporate or financial environment
- Demonstrated ability to apply advanced statistics and machine learning to deliver measurable business outcomes
- Deep expertise in statistical analysis, Python, R, and data science frameworks
- Ability to work effectively with operational teams to understand data context without needing constant translation
- Strong understanding of financial datasets and the regulatory constraints surrounding automated decision-making
- Ability to present complex quantitative findings to non-technical stakeholders
- Operator mindset with a focus on execution and analytical outcomes
Ultimus is an equal opportunity employer and does not discriminate on the basis of the applicant’s or employee’s race, color, religion, national origin, ancestry, gender, sexual orientation, age, disability, veteran or military status, genetic information, citizenship or any other status entitled to protection under federal, state or local anti-discrimination laws. No questions on our employment application are intended to secure information that is to be used for impermissible purposes.
Similar Jobs
Financial Services
Designs AI solutions for fund administration workflows, selecting models, prompts, retrieval, agents, rules, or human review. Builds evaluation datasets and metrics, runs offline and pilot evaluations, analyzes failure patterns, measures accuracy, efficiency, and cost savings, and packages successful approaches into reusable patterns.
Top Skills:
Ai AgentsArtificial IntelligenceGenerative AiIntelligent Document ProcessingLarge Language ModelsMachine LearningPredictive ModelsPrompt EngineeringRetrieval-Augmented Generation
Fintech • Software • Financial Services
Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.
Top Skills:
Evaluation PipelinesLlm ApisModel ServingObservability And Telemetry ToolingPostgresReactReact NativeRustTraining PipelinesTypescript
Analytics
Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.
Top Skills:
Agent Orchestration FrameworksAWSAzureCi/CdClaude CodeContainerizationGCPHugging FaceLarge Language ModelsModel Context ProtocolPythonPyTorchRagSQLVector Databases
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
