Data Science and Engineering

We offer expert data science and engineering services…
  • Strategic Sourcing
    • On demand Data Engineers, ML & Data Scientists for short to mid-term needs; Part-time Advisory / Consulting services
  • Data Engineering
    • Automated Data/ETL/ELT pipelines; Stream processing & Batch Processing; Workflow monitoring; Cluster Management-  running on Cloud / Hybrid environments using CI/CD principles
  • Artificial Engineering (AI) and Machine Learning (ML)
    • ML Proofs of Concept; ML Roadmaps; Interpretable ML; Business scalability; ML Pipeline latency reduction; ML Ops Automation; IoT; Turning POCs into finished products
by leveraging a broad spectrum of evolving tools and platforms..
delivered by leaders in their field..

Jeff Henrikson – Machine Learning Principal Engineer

Jeffery Stewart – Cloud & Infrastructure Services (CIS) Executive

which has translated to impressive proven capabilities…

and delivered exceptional value to our customers…
Streaming classification to assist in clinical stroke triage
  • Objective
    • A stroke clinic certified by The Joint Commission as a comprehensive stroke center would like to maintain their good standing based on “Door to CT scan” and related metrics.
  • Challenges and Implications
    • Clinical support staff loaded the Epic EHR system with valuable information on potential stroke patients while the patients are being diagnosed in clinic.
    • Yet clinicians lack software support for acting on all information known about a patient at a given time.
  • Solution
    • Analyzing the EHR record for cases in which stroke triage was not as fast as desired, a small real-time data stream was extracted from the Epic EHR “globals”, including orders, ICD codes, and textual notes.
    • A sample corpus of the data stream was annotated by experts for cases in which text messages warning for time-sensitive circumstances was desirable.
    • A classifier was trained using the annotations and connected to the clinic’s existing text messaging system.
    • Up to the second awareness of rare but significant information in the hands of clinicians.
Audit and improvement of struggling ML effort for job descriptions
  • Objective
    • In a business providing feedback on the compensation structure of other businesses, every new customer’s job descriptions needed to be matched against a set of canonical jobs.
      • The matching was performed manually by a small group of expert employees
      • Scaling the business meant growing the job matching team
  • Challenges and Implications
    • Many years of previously onboarded jobs with titles and descriptions.
    • Existing machine learning efforts toward automating the job matching task had stalled for reasons that the management did not understand.
  • Solution
    • Upon investigation, we discovered that optimizing for “Precision@K” was more appropriate and as historical matches were recorded as canonical job titles, not job identifiers, a large portion of historical job matches could no longer be interpreted with certainty. We built a model for recovering the lost matching information, for use in folding together with the newly rescoped efforts of the internal machine learning team.
    • Our efforts resulted in growth through automation instead of hiring and dramatically increased productivity of the internal machine learning team toward business objectives.