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Phd Computer Science Jobs in Minnesota (NOW HIRING)

Tactile Medical

Data Scientist

Minneapolis, MN

Bachelor's degree in data science, statistics, mathematics, computer science, economics, or a ... Master's or PhD in quantitative field Understanding of referral-based commercial models, payer ...

Abbott

MN · On-site

$50 - $56/hr

PREFERRED QUALIFICATIONS Advanced degree (Master's or PhD) in Computer Science, Data Science, Physics or Engineering. Experience in first-in-human and pivotal clinical trials. Experience and/or ...

Tactile Medical

Data Scientist

Minneapolis, MN · On-site

$100K - $150K/yr

Qualifications Required: • Bachelor's degree in data science, statistics, mathematics, computer ... PhD in quantitative field • Understanding of referral-based commercial models, payer coverage ...

Kitware

Computational Imaging Researcher

Minneapolis, MN

$116K - $137K/yr

PhD in Computer Science, Electrical Engineering, Optics, or related field * Strong publication record in top-tier research publications and conferences * Highly innovative and demonstrated track ...

Kitware

Computational Imaging Researcher

Minneapolis, MN · On-site

$125K - $170K/yr

PhD in Computer Science, Electrical Engineering, Optics, or related field * Strong publication record in top-tier research publications and conferences * Highly innovative and demonstrated track ...

Staxa Technologies

AI engineer

Minneapolis, MN · On-site

$119K - $143K/yr

Bachelor's, Master's, or PhD in Computer Science, Data Science, Mathematics, or a highly quantitative field. * Experience : 5+ years of software engineering experience, with at least 3+ years ...

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Phd Computer Science information

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$55.3K

$81.4K

$96K

How much do phd computer science jobs pay per year?

As of Jun 25, 2026, the average yearly pay for phd computer science in Minnesota is $81,398.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,900.00 and $91,600.00 per year, depending on experience, location, and employer.

Is a CS PhD worth it?

A PhD in Computer Science can lead to careers in academia, research, or specialized industry roles that require advanced technical expertise. It typically involves several years of study, research, and publication, and is valuable for positions that demand deep knowledge or innovation in areas like artificial intelligence, algorithms, or data science.

What are some common challenges faced by PhD Computer Science students during their research?

PhD Computer Science students often encounter challenges such as defining a clear and impactful research problem, managing long-term projects with limited guidance, and coping with the pressure to publish in top-tier conferences or journals. Balancing coursework, teaching responsibilities, and research can also be demanding. Effective time management, networking with peers and mentors, and seeking regular feedback can help students navigate these challenges and achieve their academic goals.

What is a PhD in Computer Science?

A PhD in Computer Science is the highest academic degree in the field, focused on advanced research and the creation of new knowledge in computing. It typically involves several years of coursework followed by original research culminating in a dissertation. Graduates often pursue careers in academia, research, or advanced industry roles that require deep technical expertise and problem-solving skills.

What are the key skills and qualifications needed to thrive as a PhD in Computer Science, and why are they important?

To thrive as a PhD in Computer Science, you need advanced expertise in algorithms, programming, and research methodologies, typically supported by a doctoral degree in computer science or a related field. Mastery of programming languages (such as Python, Java, or C++), data analysis tools, and familiarity with version control systems like Git are commonly required, along with experience in publishing academic research. Critical thinking, problem-solving, strong written and verbal communication, and perseverance are vital soft skills for success in research and collaboration. These skills and qualifications are essential for making significant contributions to the field, driving innovation, and effectively sharing knowledge with the academic and professional community.

What is the salary of a PhD in computer science?

A PhD in computer science typically earns a salary ranging from $80,000 to over $150,000 annually, depending on the industry, location, and experience. Academic positions, research roles, and industry jobs such as software engineering or data science may have different salary ranges, with industry roles generally offering higher compensation.

Can I make 200K with a computer science degree?

A PhD in Computer Science can lead to high-paying roles such as research scientists, data scientists, or senior software engineers, where salaries of $200,000 or more are achievable, especially in tech hubs or with extensive experience. However, reaching this level typically requires advanced skills, experience, and sometimes additional certifications or leadership responsibilities.

What jobs can I get with a PhD in computer science?

A PhD in computer science qualifies individuals for advanced roles such as research scientist, data scientist, machine learning engineer, and university professor. These positions often require strong analytical skills, programming expertise, and knowledge of algorithms, data structures, and AI tools. Graduates may work in academia, industry research labs, or technology companies focusing on innovation and development.
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Phd Computer Science jobs near you
Data Scientist

Data Scientist

Tactile Medical

Minneapolis, MN

Other

Posted 10 days ago

Job description

Position Summary
The Marketing Data Scientist is the predictive intelligence engine of TCMD's Marketing and Market Access organization. The primary work is finding connections in TCMD's data that no one has looked for yet, building predictive models, and translating validated models into forward-looking tools. This individual synthesizes insights from Tactile's internal and external data platforms to develop and explore hypotheses for growth. The role's mission is to surface predictive insights from these systems that inform commercial strategy before decisions are finalized. This role collaborates closely with marketing and market access leadership along with sales excellence and commercial leadership.

Accountabilities & Responsibilities
Exploratory analysis, hypothesis generation, feature engineering, model construction, and validation
Build and validate predictive models using appropriate machine learning and statistical methodologies
Translating validated models into forward-looking dashboards or automated scoring systems that are consumed with ease by stakeholders
Partner across the marketing organization to develop campaign lift attribution; building causal inference models isolating incremental referral lift from specific marketing programs
Develop predictive analytics supporting payer targeting and coverage expansion opportunities
Train commercial team users on how to interpret and act on model outputs and the specific decisions the model is designed to support
Communicate within marketing and market access on status of model pipeline and backlog; routinely collect voice of internal stakeholder needs to drive continuous improvement in data driven decision making
Manage assigned projects to completion on time, within scope, and within budget.
Other duties as assigned.

Qualifications

Required:
Bachelor's degree in data science, statistics, mathematics, computer science, economics, or a quantitative field with strong statistical foundations
4-7 years applied data science or machine learning experience, applied in commercial or operational environments
Experience creating predictive models for non-data-scientists to make real commercial or operational decisions
Comfort with messy healthcare commercial data, intellectual curiosity, and the communication discipline to translate technical findings into commercial language
Expert-level modern data science skills in Python and SQL working with structured data and machine-learning frameworks; version-controlled code development and deployment
Ability to transform messy, real-world healthcare data with missing values, inconsistent coding, and multiple granularities into reliable predictive model inputs
Working knowledge of Salesforce CRM architecture, healthcare claims data, Power BI/Fabric deployment environments

Preferred:
Master's or PhD in quantitative field
Understanding of referral-based commercial models, payer coverage dynamics, prior authorization processes, and DME/medical device reimbursement
Survival analysis experience - has applied time-to-event modeling in a commercial context (e.g., customer churn, time-to-conversion, time-to-renewal). Particularly relevant for funnel stage duration modeling and HCP churn prediction
Salesforce data architecture familiarity - understands the Salesforce object model well enough to write efficient queries and build reliable features from CRM data without requiring a Salesforce administrator to extract data
Power BI or Tableau development experience sufficient to deploy model scoring outputs as operational dashboards
Experience in a B2B2C or referral-based commercial model where the customer and the end user are different

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