6 days ago
Chicago, IL, USAMid Level / Senior
Base Salary
$85k - $100k/yr
Responsibilities
- Design, build, and iterate on applied AI and machine learning solutions including forecasting, classification, anomaly detection, NLP, and generative AI workflows.
- Independently build end-to-end proofs of concept, including data preparation, modeling, and lightweight infrastructure.
- Partner with data engineering to integrate, harden, scale, deploy, and maintain validated solutions in production.
- Define success metrics, conduct offline and online evaluations, quantify business impact, and build feedback loops for drift, regression, and misuse.
- Analyze operational data and workflows to identify opportunities for AI automation and actionable performance improvements.
- Work directly with operational leaders and frontline teams to translate business problems into AI and analytics solutions.
- Prepare datasets, engineer features, and design retrieval strategies for LLM-based systems.
- Develop, test, and deploy solutions in the Databricks Lakehouse environment using maintainable software engineering practices.
- Pilot AI tools with operational teams, incorporate user feedback, and drive adoption and continuous improvement.
- Apply responsible AI practices addressing model limitations, hallucinations, bias, privacy, and human-in-the-loop design.
Requirements
- Bachelor’s degree in analytics, data science, computer science, engineering, or a related field.
- 4–7 years of experience in analytics, data science, or AI/ML engineering, including at least two years building and deploying ML or AI solutions.
- Strong proficiency in Python and SQL, with experience writing maintainable and tested code beyond exploratory notebooks.
- Hands-on experience building applied AI or ML solutions such as predictive models, NLP, or LLM-based applications.
- Ability to independently build end-to-end proofs of concept involving data wrangling, modeling, and lightweight infrastructure.
- Experience partnering with data engineering or platform teams to move prototypes into production.
- Experience working with large datasets in modern analytics platforms such as Databricks.
- Ability to translate operational problems into analytical and AI approaches with measurable business outcomes.
- Strong communication skills with non-technical stakeholders and ability to explain AI behavior, limitations, and results.
- Preferred experience with generative AI, LLM APIs such as OpenAI or Anthropic, RAG systems, or agentic workflows.
- Preferred familiarity with MLOps tooling and practices including MLflow, model registries, ML CI/CD, and monitoring or observability.
- Preferred experience designing AI evaluation frameworks, including offline benchmarks and online experimentation.
- Preferred experience in operational, services, or asset-heavy environments and exposure to predictive modeling, time series analysis, or NLP.
- Preferred familiarity with Databricks Lakehouse concepts and collaborative analytics workflows.
- Preferred track record of driving adoption of analytics or AI tools and managing multiple concurrent initiatives.
Benefits
- Eligible employees may enroll in health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life insurance, and disability insurance programs.
- Paid and unpaid time away from work are available to eligible employees.
- Competitive pay varies based on factors including location, hire date, hours worked, job type, business line, collective bargaining coverage, market rates, experience, and qualifications.
Categories
