Sr. Data Scientist

Sr. Data Scientist

Los Angeles, CA (Remote)

12 Months

We've noticed that many of the resumes submitted so far are missing some crucial experience.

Specifically :

• Healthcare Data and Clinical Research Outcomes : The resumes lack explicit experience in these areas, which are key focuses of the job.
• Experimental Frameworks and Healthcare Analytics : While candidates have implemented advanced AI / ML systems, they haven't highlighted their experience in creating experimental frameworks for data collection or healthcare-specific analytics.
• Healthcare-Centric AI Tools and Compliance : There's limited mention of healthcare-centric AI tools or familiarity with FHIR, EDI formats, or healthcare compliance frameworks like HIPAA.

Additional REQUIRED qualifications :

Familiarity and experience with techniques for handling outliers, such as the z-score or IQR methods.

Experience with multiple machine learning models, beyond tree-based methods, and familiarity with handling imbalanced data problems using appropriate techniques.

Strong grasp of model evaluation metrics, including a correct understanding of the F1 score.

Experience with PDF to text conversion, NLP entity extraction, ML modeling.

Configure large language models (LLM) including RAG implementation and fine tuning LLMs to meet business requirements.

Data mining and statistical modeling (e.g. regression modeling, clustering techniques, decision trees, etc.).

Analyzing large data dets using programming languages (e.g. Python, R, SQL and / or Spark).

Strong SQL skills. Must have the ability to write complex, highly-optimized queries across large volumes of data. Generating and visualizing data-based insights in software such as Tableau.

Effective at communicating data-driven insights and conveying actionable recommendations to clinical and business leaders with little to no data science experience.

Lead and mentor junior data scientists to extract, analyze, and interpret large datasets, particularly focusing on clinical healthcare operational, and environmental data.

Technical Expertise :

Proficiency in developing and implementing artificial intelligence (AI) and machine learning (ML) algorithms.

Experience in building highly scalable AI systems with multiple dependencies across teams.

Ability to utilize state-of-the-art AI algorithms and apply off-the-shelf AI and data-centric tools.

Expertise in managing and analyzing large datasets, including data cleaning, transformation, and integration.

Data Science Skills :

Strong skills in statistical analysis and deriving insights from patient and environmental data.

Capability to correlate similar data, find actionable results, and search large datasets for usable information.

Experience in creating experimental frameworks for data collection and automation of data processes.

Collaboration and Communication :

Proven ability to work closely with research teams to design analysis specifications, including algorithms and interpretation of results.

Experience in collaborating with programmers and informatics experts to address client needs.

Ability to create clear, concise reports and presentations to communicate analysis results effectively.

Healthcare Focus :

Familiarity with healthcare data, including clinical and research outcomes.

Ability to identify creative uses of AI innovations in healthcare research.

Additional Requirements :

Demonstrated ability to stay current on trends in informatics, AI, and ML within the healthcare sector.

The Data Scientist to derive insights from the vast amounts of patient and environmental data available within our data warehouse. To achieve this goal, Client and creative machine learning and statistical analyses are needed.

The incumbent will work closely with Client researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.

The incumbent will develop and implement algorithms on existing data warehouse records and may nominate new external data sources to be ingested to the data warehouse to strengthen analyses.

Analyses will address a wide variety of clinical and research outcomes.

The incumbent will research and implement state-of[1]the-art AI algorithms, apply off-the-shelf AI and data-centric tools, and collect, store, and maintain data.

The successful candidate will have demonstrated competence in developing highly scalable artificial intelligence systems with multiple dependencies across teams.

Medical Enterprise - Non-Clinical Onboarding - Must complete all onboarding requirements prior to the start date, with no exceptions.