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About This Role
Company: TESA Research Inc.
Location: Park City, UT (on-site/hybrid/remote)
Reports To: VP of Cancer Genomics
Employment Type: Full-Time
About TESA Research
TESA Research Inc. is an early-stage research company developing proprietary computational and analytical approaches to advance the study and treatment of cancer. We are building a world-class team to translate genomic insight into actionable biological and therapeutic understanding.
Position Summary
TESA Research is seeking a Cancer Genomics Data Scientist to identify, retrieve, and prepare genomic and clinical data from public and proprietary sources for use in TESA's proprietary software platform. This role works in close collaboration with the VP of Cancer Genomics and can draw on the software engineering team for platform-level support, but is expected to independently perform the core data identification, extraction, and transformation work that feeds the platform. This is a hands-on, detail-oriented role for someone who is comfortable working across public repositories, proprietary datasets, and internal tooling to get data analysis-ready.
Key Responsibilities
- Data Acquisition: Identify and download relevant genomic and clinical datasets from public repositories (e.g., TCGA, GEO, cBioPortal, SRA, ICGC) and proprietary/licensed databases.
- Data Preparation & Transformation: Clean, curate, harmonize, and format genomic and clinical data so it is compatible with TESA's proprietary software platform, independently handling the majority of data transformation work.
- Pipeline Execution: Run and manage DNA and RNA analysis pipelines (e.g., alignment, variant calling, expression quantification) to generate processed datasets ready for downstream analysis.
- Platform Loading & QC: Load prepared datasets into TESA's platform, verify data integrity and formatting, and troubleshoot discrepancies before results are generated.
- Scientific Collaboration: Work closely with the VP of Cancer Genomics to understand data and analysis requirements, prioritize datasets, and ensure outputs support the company's scientific objectives.
- Cross-Functional Support: Partner with the software engineering team on platform-related data ingestion issues, flagging technical blockers while independently resolving data-level issues.
- Documentation: Maintain clear documentation of data sources, provenance, transformation steps, and pipeline versions to ensure reproducibility and auditability.
Qualifications
Required:
- Advanced degree in Bioinformatics, Computational Biology, Genomics, or a related field; PhD preferred
- Hands-on experience with DNA and RNA analysis pipelines (e.g., differential expression analysis, GSEA, CNV calling, ASCAT)
- Experience working with public and proprietary genomic and clinical data repositories
- Solid understanding of cancer genomics and the structure of associated genomic/clinical datasets
- Proficiency in scripting/programming for data wrangling (e.g., Python, R, or equivalent) and command-line bioinformatics tools
- Ability to work independently on data transformation tasks while collaborating effectively with scientific and engineering colleagues
- Strong attention to detail and comfortable working in a fast-moving, resource-conscious early-stage company environment
Preferred:
- Experience preparing data for a proprietary or internal software/analytics platform
- Familiarity with cytogenetic or pathway-level data and their integration with genomic datasets
- Experience with cloud-based or high-performance computing environments for large-scale genomic data processing
- Prior experience in a startup or small-team research environment
What We Offer
- Meaningful equity ownership in an early-stage company with high growth potential
- Competitive base salary commensurate with experience and company stage
- Significant autonomy and flexibility in how and when you work
- Direct influence over company strategy and direction as a member of the founding/early leadership team
- The opportunity to build something from the ground up and shape the scientific direction of a growing cancer genomics company
- Formal benefits package to be established as the company scales
TESA Research Inc. is an equal opportunity employer.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At TESA Research, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
TESA Research AI Hiring
TESA Research has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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