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Data Science Jobs in Vermont (NOW HIRING)

The role involves defining data science operations strategy, architecting scalable ML pipelines, and overseeing AWS Data Lake architectures. Responsibilities : • Lead and manage a team of 6 data ...

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

New

Role Summary The data science (DS) internship at Crowe follows the firmwide calendar, approximately overlapping the academic summer. DS interns will have a designated data scientist mentor and will ...

New

... Data Science, and Data Governance - Architecting and implementing cloud-based solutions meeting industry standards Travel Requirements Up to 60% Job Posting End Date The salary range for this ...

As a Manager, you will lead teams of data scientists and ML engineers, manage client relationships, and translate complex business challenges into AI-driven strategies and solutions. This role offers ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

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Showing results 1-20

Data Science information

See Vermont salary details

$39.9K

$130.5K

$208.9K

How much do data science jobs pay per year?

As of Aug 16, 2026, the average yearly pay for data science in Vermont is $130,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,700.00 and $144,600.00 per year, depending on experience, location, and employer.

Is a data scientist in high demand?

Yes, data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

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.

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.

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 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 are the most commonly searched types of Data Science jobs in Vermont?

The most popular types of Data Science jobs in Vermont are:

What are popular job titles related to Data Science jobs in Vermont?

For Data Science jobs in Vermont, the most frequently searched job titles are:

What job categories do people searching Data Science jobs in Vermont look for?

The top searched job categories for Data Science jobs in Vermont are:

What cities in Vermont are hiring for Data Science jobs?

Cities in Vermont with the most Data Science job openings:

Infographic showing various Data Science job openings in Vermont as of August 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 100% In-person job distribution, with an average salary of $130,502 per year, or $62.7 per hour.

AWS Cloud Data Lake Lead

Diverse Lynx

Cambridge, VT • On-site

Full-time

Re-posted 11 days ago


Job description

Job Summary:
Diverse Lynx is seeking an AWS Cloud Data Lake Lead to manage a team of data scientists and ML engineers. The role involves defining data science operations strategy, architecting scalable ML pipelines, and overseeing AWS Data Lake architectures.
Responsibilities:
• Lead and manage a team of 6 data scientists, ML engineers, and analytics professionals across onshore/offshore locations, providing technical mentorship and career guidance.
• Define and drive the data science operations strategy, roadmap, and best practices aligned with business objectives.
• Partner with senior business stakeholders, product owners, and cross-functional teams to identify high-impact AI/ML opportunities and translate them into actionable project plans.
• Establish and govern standards for model development, deployment, monitoring, and responsible AI adoption across the organization.
• Architect and oversee scalable ML pipelines for data ingestion, feature engineering, model training, validation, and inference on AWS cloud and Databricks.
• Design and implement AWS Data Lake architectures and big data processing solutions for structured and unstructured data at petabyte scale using Spark, Databricks, and AWS-native services (S3, Lake Formation, EMR, Glue, SageMaker, Redshift, Athena).
• Lead the deployment of production ML systems including real-time inference APIs, batch prediction pipelines, and model-as-a-service architectures.
• Drive MLOps maturity CICD for ML, automated model retraining, drift detection, AB testing, and performance monitoring.
Qualifications:
Required:
• Deep hands-on experience with AWS services SageMaker, S3, EMR, Glue, Lambda, Redshift, Athena, Step Functions, Lake Formation, and IAM security best practices.
• Proven experience designing and managing AWS Data Lake architectures.
• Proficiency in Databricks for large-scale data engineering, ML model development, MLflow for experiment tracking, and Unity Catalog for governance.
• Strong experience with Apache Spark (PySpark/Scala), distributed computing, and processing large-scale structured/unstructured datasets.
• Solid expertise in ML algorithms, feature engineering, model evaluation, and frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Proven experience with end-to-end ML lifecycle model deployment, monitoring, retraining, CICD pipelines, containerization (Docker), and orchestration (Kubernetes/ECS).
• Expert-level proficiency in Python for end-to-end data science and ML workflows.
• Advanced SQL for data analysis and pipeline development.
• Proficiency with Git, branching strategies, and code review practices.
• Lead and manage a team of 6 data scientists, ML engineers, and analytics professionals across onshore/offshore locations, providing technical mentorship and career guidance.
• Define and drive the data science operations strategy, roadmap, and best practices aligned with business objectives.
• Partner with senior business stakeholders, product owners, and cross-functional teams to identify high-impact AI/ML opportunities and translate them into actionable project plans.
• Establish and govern standards for model development, deployment, monitoring, and responsible AI adoption across the organization.
• Architect and oversee scalable ML pipelines for data ingestion, feature engineering, model training, validation, and inference on AWS cloud and Databricks.
• Design and implement AWS Data Lake architectures and big data processing solutions for structured and unstructured data at petabyte scale using Spark, Databricks, and AWS-native services (S3, Lake Formation, EMR, Glue, SageMaker, Redshift, Athena).
• Lead the deployment of production ML systems including real-time inference APIs, batch prediction pipelines, and model-as-a-service architectures.
• Drive MLOps maturity CICD for ML, automated model retraining, drift detection, AB testing, and performance monitoring.
Company:
Diverselynx IT Consulting Services Founded in 2002, the company is headquartered in Princeton, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Diverse Lynx logo

About Diverse Lynx

Sourced by ZipRecruiter

Diverse Lynx, based in Princeton, NJ, US, is a reputable company in the Information Technology sector. The firm, as reflected through its website diverselynx.com, specializes in delivering comprehensive IT solutions. These solutions range from IT consulting to robust digital transformation strategies, IT staffing, and full-time placements services. The company was established in 2008, and it prides itself on providing simplified, efficient technology solutions designed to meet the unique needs of each client.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Princeton, NJ, US

Year founded

2002

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