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Data Science Phd Jobs (NOW HIRING)

Your Mission We are seeking a Manager of Data Science to lead our talented team of data scientists ... Advanced degree (Master's or PhD) in a quantitative field (CS, Stats, Math, Physics) is a plus.

S., PhD, or equivalent experience) in a quantitative field (e.g., CS, ML, Statistics, OR, Applied Math, Data Science), or 10+ years building and leading large-scale ML/optimization systems. * Proven ...

S., PhD, or equivalent experience) in a quantitative field (e.g., CS, ML, Statistics, OR, Applied Math, Data Science), or 10+ years building and leading large-scale ML/optimization systems. * Proven ...

Required : • PhD in a quantitative field (Statistics, Computer Science, Economics, Operations ... distributed data systems (e.g. Snowflake) through research or internships • Ability to ...

Director- Data Science

Bellevue, WA · On-site

$156K - $312K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

Director- Data Science

Bellevue, WA · On-site

$156K - $312K/yr

Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful ...

The Director, Data Science will lead efforts across personalization, recommendation systems, and ... Master's Degree or PhD in a quantitative field (math, computer science, engineering, etc.) required.

The Director, Data Science will lead efforts across personalization, recommendation systems, and ... Master's Degree or PhD in a quantitative field (math, computer science, engineering, etc.) required.

$162 - $185/hr

Team Description The Bank Operations Data Science team builds the machine learning models that help ... A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics ...

PhD or Master's in a quantitative field and 8+ years of tech or energy industry work experience as a statistician, quantitative analyst, or data scientist. * 5+ years of experience directly managing ...

PhD or Master's degree in Computer Science, Statistics, Mathematics, or related quantitative field ... Experience building data science teams from scratch or through periods of rapid growth * Prior work ...

PhD or Master's in a quantitative field and 8+ years of tech or energy industry work experience as a statistician, quantitative analyst, or data scientist. * 5+ years of experience directly managing ...

Showing results 21-40

Data Science Phd information

What is a data science PhD?

A Data Science PhD is a doctoral-level degree focused on advanced research in data science, which combines elements of statistics, computer science, and domain expertise. Students in a Data Science PhD program typically work on developing new methods for analyzing large datasets, creating machine learning algorithms, and addressing complex problems in areas such as artificial intelligence, data mining, and predictive analytics. Graduates are prepared for careers in academia, research, and industry, where they can lead data-driven projects and contribute to advancements in the field.

What are the key skills and qualifications needed to thrive as a data science PhD?

To thrive as a Data Science PhD, you need advanced expertise in statistics, machine learning, data analysis, and a doctoral degree in a quantitative field. Proficiency in programming languages like Python or R, experience with big data frameworks (e.g., Spark, Hadoop), and familiarity with data visualization tools are typically required. Critical thinking, problem-solving, and strong communication skills help you translate complex data insights for diverse stakeholders. These skills are vital for driving innovative research, making data-driven decisions, and contributing impactful solutions in data-centric environments.

What are some common challenges faced by data science PhDs when transitioning from academia to industry roles?

Data Science PhDs often encounter challenges such as adapting to the faster pace and collaborative nature of industry projects compared to academic research. In industry, there is a greater emphasis on delivering practical solutions within tight deadlines and working closely with cross-functional teams like engineering and product management. Additionally, data science work in industry may require balancing technical rigor with business impact, often prioritizing actionable insights over exhaustive analysis. Building strong communication and stakeholder management skills can help ease this transition.

What can I do with a data science PhD?

A data science PhD prepares individuals for advanced roles in research, analytics, and machine learning across industries such as technology, finance, healthcare, and academia. Graduates can work as data scientists, machine learning engineers, research scientists, or data analysts, often utilizing programming languages like Python or R and tools such as TensorFlow or SQL. The degree also enables roles involving complex data modeling, statistical analysis, and developing innovative data-driven solutions.
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What cities are hiring for Data Science Phd jobs?

Cities with the most Data Science Phd job openings:

What states have the most Data Science Phd jobs?

States with the most job openings for Data Science Phd jobs include:

Infographic showing various Data Science Phd job openings in the United States as of August 2026, with employment types broken down into 33% Internship, and 67% Full Time. Highlights an 100% In-person job distribution.

Data Scientist - Innovation - PhD

Caris Life Sciences

Irving, TX • On-site

Full-time

Re-posted 9 days ago


Job description

At Caris, we understand that cancer is an ugly word-a word no one wants to hear, but one that connects us all. That's why we're not just transforming cancer care-we're changing lives.

We introduced precision medicine to the world and built an industry around the idea that every patient deserves answers as unique as their DNA. Backed by cutting-edge molecular science and AI, we ask ourselves every day:"What would I do if this patient were my mom?"That question drives everything we do.

But our mission doesn't stop with cancer. We're pushing the frontiers of medicine and leading a revolution in healthcare-driven by innovation, compassion, and purpose.

Join us in our mission to improve the human condition across multiple diseases. If you're passionate about meaningful work and want to be part of something bigger than yourself, Caris is where your impact begins.

Position Summary

Want to help build AI models for the next generation of cancer diagnostics? The models you build here have direct line-of-sight to translational research and clinical decision-making -- work with the potential to shape how cancer is detected, profiled, and treated. As a Data Scientist on the Innovation Team, you will develop machine learning and deep learning algorithms on molecular sequencing data (WGS, WES, RNA-seq, cfDNA), design analytic pipelines for novel biomarker discovery, and tackle the most challenging problems in liquid biopsy and translational oncology research.

About the Team

The Innovation Team is a small, fast-moving R&D group within Caris Life Sciences, drawing on proprietary clinical research data that no other team in oncology can match. We work closely with bioinformaticians, molecular biologists, and clinical scientists to develop high-impact AI models with the potential to shift the landscape of clinical outcomes. You will have the freedom to lead research projects end-to-end -- from problem framing to deployment -- and to shape the methods that drive Caris' R&D agenda. In your first year, success looks like leading one or two research projects from problem framing through deployment, contributing to a peer-reviewed publication or conference submission, and helping shape methods that inform Caris' diagnostic platform.

Job Responsibilities

  • Processing, manipulating, and analyzing large diverse datasets generated from NGS to develop biomarkers for cancer diagnosis, prognosis, and treatment.

  • Developing novel algorithms for feature extraction and biomarker discovery from molecular sequencing data.

  • Applying first-principles analysis to translate open research questions into tractable, well-defined problems.

  • Applying state-of-the-art machine learning and deep learning methods to biological and clinical research questions.

  • Creating rigorous evaluation frameworks and tracking experiments systematically using tools such as MLflow or Weights & Biases.

  • Authoring peer-reviewed research publications and presenting findings at scientific conferences.

Required Qualifications

  • PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or a related quantitative or biological field.

  • PhD recently completed, or up to approximately 2 years of post-doctoral research experience (academic or industry).

  • Demonstrated work on a cancer biology or translational research problem (PhD thesis chapter, peer-reviewed publication, or postdoc / industry role).

  • Hands-on experience with molecular sequencing data (e.g., WGS, WES, RNA-seq, cfDNA) including production-grade pipelines and analysis.

  • Hands-on experience with generative AI -- large language models, foundation models (e.g., genomic or protein language models), or agentic workflows applied to scientific or clinical data.

  • Proficiency with PyTorch and modern deep learning architectures (transformers, attention mechanisms), with demonstrated application of ML/DL to biological or clinical data.

  • First-author or co-first-author peer-reviewed publications in machine learning venues (e.g., NeurIPS, ICML, ICLR) or in bioinformatics / computational biology journals.

  • Strong Python; comfortable in Linux; proficient with git and collaborative workflows.

Preferred Qualifications

  • Multi-omics integration experience (genomics, transcriptomics, proteomics, methylation, etc.).

  • Experience with epigenetics -- DNA methylation analysis, chromatin biology, or related.

  • Interest in cell-free DNA, liquid biopsy, and next-generation early cancer diagnostics.

  • Interest in novel algorithm development for biomedical signal extraction in sequencing data.

  • Proficiency in cloud platforms (AWS EC2, S3, HealthOmics) and containerization (Docker).

Physical Demands

  • This role primarily involves sedentary work at a computer workstation, including extended periods of typing, reading screens, and virtual or in-person collaboration. Caris provides reasonable accommodations to qualified individuals with disabilities; candidates who need accommodation during the application or interview process are encouraged to contact Caris HR.

Training

All job-specific, safety, and compliance training are assigned based on the job functions associated with this employee.

Other

  • This position is on-site in Irving, TX. The team operates on a fast-iteration research cycle that benefits from close, in-person collaboration.

  • Relocation assistance may be available for qualified candidates.

Conditions of Employment: Individual must successfully complete pre-employment process, which includes criminal background check, drug screening, credit check( applicable for certain positions) and reference verification.

This job description reflects management's assignment of essential functions. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities to this job at any time.

Caris Life Sciences is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.