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Data Science Jobs in Quincy, MA (NOW HIRING)

FocusKPI Inc.

Data Science Intern

Boston, MA · On-site +1

Data Science Intern Statistical Modeling & Marketing Measurement Remote | Internship | Full-time | 3 months About the Role We are looking for a curious and analytically minded Data Science Intern to ...

Formation Bio

Director, Data Science

Boston, MA · On-site

$235K - $307K/yr

About the Position As the Director of Data Science at Formation Bio, you will be at the forefront of revolutionizing drug development through AI and advanced analytics. In this role, you'll lead ...

Novartis

Associate Director, Data Science

Cambridge, MA · Hybrid

$160K - $297K/yr

Cambridge, MA Novartis is a leader in data science and model-informed drug development. We are seeking an experienced Data Science leader to advance data-driven drug discovery and development by ...

ShiftCode Analytics, Inc.

Data Science Engineer

Boston, MA · On-site

$124K - $149K/yr

ShiftCode Analytics, Inc. is seeking a talented Data Science Engineer to join their team and contribute to the development and implementation of advanced data solutions. The role involves designing ...

Servier

Scientist II, Data Science

Boston, MA · On-site

$146K - $165K/yr

... our Data Science team. The Scientistwill be performing Next Generation Sequencing (NGS) data analysis and interpretation for multiple Oncology programs. This individual will be integrated with ...

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All JobsData Science JobsData Science Jobs in Quincy, MA

Data Science information

See Quincy, MA salary details

$39.4K

$129.1K

$206.6K

How much do data science jobs pay per year?

As of Sep 8, 2026, the average yearly pay for data science in Quincy, MA is $129,054.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,600.00 and $143,000.00 per year, depending on experience, location, and employer.

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 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 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 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.

Is a data scientist in high demand?

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 jobs can a data scientist do?

A data scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.
More about Data Science jobs

What are the most commonly searched types of Data Science jobs in Quincy, MA?

The most popular types of Data Science jobs in Quincy, MA are:

What are popular job titles related to Data Science jobs in Quincy, MA?

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

What job categories do people searching Data Science jobs in Quincy, MA look for?

The top searched job categories for Data Science jobs in Quincy, MA are:

What cities near Quincy, MA are hiring for Data Science jobs?

Cities near Quincy, MA with the most Data Science job openings:

Data Science jobs near you
Infographic showing various Data Science job openings in Quincy, MA as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 15% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $129,054 per year, or $62 per hour.

Data Science Intern

FocusKPI Inc.

Boston, MA • On-site, Remote

Full-time

Posted 10 days ago

Job description

Data Science Intern
Statistical Modeling & Marketing Measurement
Remote | Internship | Full-time | 3 months
About the Role
We are looking for a curious and analytically minded Data Science Intern to support the development and evaluation of statistical and machine learning models for marketing measurement. This role is designed for someone with strong quantitative fundamentals who wants hands-on experience applying regression, model diagnostics, validation, and data analysis to real business problems. You will work closely with experienced data scientists, learn how modeling choices affect interpretation and business decisions, and contribute clean, reproducible analytical work.
Responsibilities
  • Support the development and evaluation of models including regression, time-series, and other statistical or machine learning approaches, with attention to predictive performance, stability, and interpretability.
  • Prepare and explore data using Python and SQL; perform data-quality checks, feature construction, descriptive analysis, and visualization to understand modeling inputs and outcomes.
  • Apply core model-validation techniques such as train/validation/test splits, cross-validation, baseline comparisons, and appropriate performance metrics.
  • Investigate common statistical issues including multicollinearity, overfitting, residual patterns, autocorrelation, heteroskedasticity, and unstable coefficients, with guidance from senior team members.
  • Test and compare reasonable modeling choices such as feature transformations, regularization settings, and model specifications, and summarize how these choices affect model results.
  • Interpret model outputs and connect technical findings to practical marketing or business questions while clearly stating assumptions and limitations.
  • Contribute to reproducible analytical workflows for model training, validation, sensitivity checks, and result comparison.
  • Write clear Python and SQL code and communicate methods, findings, assumptions, and open questions in a structured and understandable way.

Basic Qualifications
  • Strong foundation in statistics and regression: understanding of linear regression, key model assumptions, coefficient interpretation, regularization concepts, and basic statistical inference.
  • Solid quantitative fundamentals in probability, statistics, and linear algebra; familiarity with calculus or optimization concepts is helpful.
  • Working knowledge of Python for data analysis and modeling, including common data-science libraries; basic to intermediate SQL skills for data extraction and transformation.
  • Understanding of model evaluation: training versus validation data, cross-validation, common regression metrics, overfitting, and the importance of out-of-sample performance.
  • Ability to reason through modeling problems: investigate unexpected results, form hypotheses about root causes, test alternatives, and explain conclusions using evidence.
  • Clear communication skills: ability to explain analytical methods, assumptions, results, and limitations to technical teammates and learn from feedback.
  • Currently pursuing a degree in statistics, computer science, data science, machine learning, applied mathematics, econometrics, operations research, or a closely related quantitative field.

Preferred Qualifications
  • Coursework, research, or project experience using regression, time-series analysis, statistical modeling, or machine learning.
  • Exposure to Marketing Mix Modeling (MMM), marketing analytics, attribution, or other measurement problems.
  • Basic understanding of concepts such as adstock, saturation, incremental impact, ROI, or response curves.
  • Familiarity with A/B testing, causal inference, simulation, sensitivity analysis, or confidence intervals.
  • Experience with Python libraries such as pandas, NumPy, statsmodels, scikit-learn, SciPy, or similar tools.
  • Previous internship, research assistantship, academic project, or independent project involving real-world data is a plus.

NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.
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About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

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