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Data Science Jobs in Manchester, VT (NOW HIRING)

Iterative Health

Staff Data Scientist

Cambridge, NY · On-site

This role sits at the intersection of data science, product strategy, and ML: you'll lay the foundation for our predictive capabilities and shape what that function becomes. This is an opportunity ...

Iterative Health

Staff Data Engineer

Cambridge, NY

Familiarity with clinical trial operations, EDC systems, or life sciences data * SOC 2, HIPAA or similar compliance experience baked into engineering practice * A track record of building or ...

Schrodinger

Solutions Architect

Cambridge, NY · On-site

$105K - $155K/yr

Research data management background in the pharmaceutical industry (or similar scientific data space) with a focus or specialization in Biologics data * Experience with Biologics discovery platforms ...

Navtech, Inc.

"Data Engineer"

Hartford, NY · On-site

$113K - $135K/yr

Data Engineer with GCP Location : Preferably Wellesley, MA, Hartford, CT, or NYC. Will Consider (Remote). Duration: 12 months Must Haves: • 5+ Years of Experience • Unix/Linux • Pig (Hive good ...

Hollingsworth ＆ Vose

R&D Technician

Easton, NY · On-site

Record and summarize test data and observations from product and raw material testing; proficiency ... Influence other technicians and scientists at more than current site location * Carries out ...

OrthoNY

Medical Assistant

Saratoga Springs, NY · On-site

$18 - $26/hr

Medical Assistance Certification or Degree in the Sciences preferred * 1-3 years of experience in a medical setting preferred; * Excellent communication and customer service skills; * Strong data ...

Iterative Health

Director, Compliance

Cambridge, NY

Maintain comprehensive compliance with GDPR, HIPAA, and other applicable data privacy regulations ... Bachelor's degree in Life Sciences, Business, or a related field plus 8+ years of compliance ...

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All JobsData Science JobsData Science Jobs in Manchester, VT

Data Science information

See Manchester, VT salary details

$36.9K

$120.7K

$193.3K

How much do data science jobs pay per year?

As of Jun 15, 2026, the average yearly pay for data science in Manchester, VT is $120,717.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,900.00 and $133,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

Is AI replacing data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not replace the need for skilled professionals to interpret complex data, develop models, and make strategic decisions. Data scientists with expertise in programming, statistical analysis, and machine learning remain essential for designing and deploying AI solutions effectively.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What jobs are there in data science?

Data science offers a variety of roles including Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer, and Business Intelligence Analyst. These positions typically require skills in programming, statistics, and data visualization tools, and may involve working with large datasets, predictive modeling, and data-driven decision making.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

What jobs does a data scientist do?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use programming languages like Python or R, employ statistical techniques, and often work with machine learning algorithms to solve complex problems across various industries.
More about Data Science jobs
What cities near Manchester, VT are hiring for Data Science jobs? Cities near Manchester, VT with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Manchester, VT as of June 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $120,717 per year, or $58 per hour.

Staff Data Scientist

Iterative Health

Cambridge, NY • On-site

Other

Posted 21 days ago

Job description

About the Role

Accelerating clinical research is one of the defining challenges in healthcare. Promising therapies exist that patients can't access because the operational infrastructure to run clinical trials efficiently doesn't exist yet. We're building it. That means designing technology systems that bring order to a fragmented landscape of clinical data sources, automating the operational work that slows trials down, and turning real-world clinical data into a foundation for predictive intelligence.

We're looking for a Staff Data Scientist to be the person who understands our data deeply enough to know what's possible and curious enough to prove it. We have a truly unique data set within the industry, connecting clinical data (emr, endoscopic video, etc...) to trial data across 80+ trial sites. We're looking for someone who wants to dig deeply into this data - to understand its structure, its gaps, what it can tell us - and connect that understanding to real outcomes for sites and patients. The landscape is evolving rapidly, and the right person will have a point of view on how to apply new capabilities to our specific data and problems as they emerge.You'll work hands-on with the data, structure experiments, evaluate what's modelable, and directly influence what we build and how. This role sits at the intersection of data science, product strategy, and ML: you'll lay the foundation for our predictive capabilities and shape what that function becomes.

This is an opportunity for someone who wants to be part of a small, fast-moving engineering team at a formative stage. You'll shape what gets built, how decisions get made, and what the team becomes.

Responsibilities

  • Work with clinical, video, and clinical trial operational data to understand what's there, what's meaningful, and how we can use it to drive a more efficient clinical trial system
  • Design and run experiments that determine what's worth building
  • Stay close to the evolving ML and model landscape and bring a point of view on how new capabilities apply to our data and problems
  • Define the path from raw data to product and operationalization: what to model, how to evaluate it, and when it's ready to ship
  • Partner with product and engineering to translate findings into concrete product decisions
  • Identify opportunities where our data creates unique predictive advantages
  • Evaluate where we should build, where we should partner, and where existing approaches fall short
  • Help shape the engineering culture of a small, growing team: how technical decisions get made, how problems get debated, what rigor looks like in practice

What We're Looking For

Required Qualifications

  • 5+ years of experience in data science, applied ML, or quantitative research, with significant time spent hands-on with data
  • Experience with healthcare data, clinical research, or life sciences
  • A deep curiosity to understand data and connect it to the real world
  • Deep experience designing and running experiments: you know how to structure a question, test it honestly, and draw conclusions that hold up
  • Strong statistical foundations and the judgment to know when a result is meaningful versus interesting
  • Fluent in Python and SQL, comfortable working across the data stack from exploratory analysis through to production-ready pipelines
  • Experience working across data science, product, and engineering: you can influence what gets built, not just analyze what exists
  • Have worked with complex, messy, real-world data and know how to make it useful without pretending it's clean
  • Knowledgeable about ML capabilities and frameworks, with the judgment to know when something is genuinely applicable versus hype
  • Can communicate findings clearly to technical and non-technical audiences, and make a persuasive case for what to build next

Preferred Qualifications

  • Experience working at growth stage startups (strongly preferred)
  • Experience with medical imaging or video data
  • Familiarity with clinical trial operations, disease classification, or patient identification problems
  • Experience building or defining the roadmap for an ML function from early stages
    • A track record of data science work that directly changed product direction

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