Best Remote Entry Level Data Science Jobs

Best Remote Entry Level Data Science Jobs at Built In — Research Report

Introduction

Best Remote Entry Level Data Science Jobs is a practical starting point for candidates seeking early-career opportunities in analytics, machine learning, experimentation, and business intelligence. Built In functions as a technology recruitment and employer reputation platform, so its data science listings represent opportunities across many employers rather than one standardized internal job description.

The application period is flexible: Deadline: Varies by listing. Some openings remain active until a hiring team reaches its applicant target, while others close quickly after receiving qualified candidates. The strongest strategy is to apply soon after a role appears, then tailor your resume and portfolio to the specific company, industry, and technical requirements.

Overview of Built In

Built In is a technology-focused recruitment and employer branding platform. Its services connect job seekers with companies hiring across software, artificial intelligence, cybersecurity, finance technology, healthcare technology, marketing technology, and related fields. The platform also publishes career guidance and company content designed to help candidates understand employers before applying.

Built In occupies a niche between a broad job board and a specialized technology career community. Its listings often include detailed information about work location, salary, seniority, benefits, and company culture. Current listings show that remote and hybrid data science roles can span multiple U.S. locations and compensation levels, although many visible postings are mid-career or senior rather than entry level.

The company describes itself as an established business operating with startup energy, emphasizing speed, customer proximity, innovation, and a willingness to challenge conventional recruitment practices. That positioning matters to applicants: candidates are generally expected to be comfortable with change, digital products, measurable outcomes, and cross-functional collaboration.

People use Built In because it concentrates technology opportunities in one searchable environment and often provides more context than a basic job advertisement. For early-career data scientists, the platform is especially useful for comparing employers, identifying recurring skill requirements, and understanding whether “remote” means fully distributed, location-restricted, or remote with occasional office expectations.

Best Remote Entry Level Data Science Jobs Role

Role Overview

The title describes a category of opportunities rather than a single standardized position. An entry-level data scientist may support product analytics, customer insights, forecasting, experimentation, fraud detection, operations research, or machine learning. The daily work usually combines data preparation, statistical analysis, visualization, model development, and communication with stakeholders.

The business impact can be substantial. A well-designed analysis may help a product team improve conversion, help a sales organization prioritize accounts, or help operations leaders reduce delays. At the entry level, success rarely means building a sophisticated model alone; it means producing reliable analysis that helps a team make a better decision.

Detailed Responsibilities

• Extract, clean, join, and validate data from databases, product systems, spreadsheets, or application programming interfaces.
• Write SQL queries and Python or R scripts for recurring analysis and exploratory work.
• Build dashboards, reports, and visualizations that make trends understandable to nontechnical partners.
• Analyze experiments, customer behavior, retention, acquisition, pricing, or operational performance.
• Develop baseline predictive models and compare them using appropriate evaluation metrics.
• Document assumptions, data limitations, methodology, and recommendations.
• Collaborate with product managers, engineers, marketers, finance teams, and senior data professionals.
• Monitor data quality and investigate unusual changes in metrics or model performance.

Day-to-Day Workflow

A realistic day may begin with a team stand-up or written status update, followed by focused work in a notebook or SQL editor. A candidate might spend the morning checking whether a dataset is complete, then meet with a product manager to clarify a business question. The afternoon could involve building a dashboard, reviewing model results, or presenting a concise recommendation.

Remote work adds a communication requirement. Strong employees make their work visible through clear documentation, concise messages, organized notebooks, and predictable updates. They do not wait until the end of a project to disclose that a data source was incomplete or that an analysis depends on an uncertain assumption.

Tools and Technologies

The exact stack varies by employer, but applicants should expect some combination of SQL, Python, pandas, scikit-learn, Jupyter, Git, and a cloud data warehouse such as Snowflake, BigQuery, or Redshift. Visualization tools may include Tableau, Looker, Power BI, or a Python library such as Matplotlib or Seaborn.

Some roles emphasize experimentation and product analytics, while others use machine learning, forecasting, natural language processing, or data engineering tools. Do not claim familiarity with every platform. Demonstrating strong fundamentals and the ability to learn an unfamiliar tool is more credible than listing technologies without evidence.

Skills and Requirements

Technical Skills

• SQL, including joins, common table expressions, aggregations, window functions, and basic query optimization.
• Python or R for data cleaning, statistical analysis, visualization, and reproducible workflows.
• Foundational statistics, including sampling, confidence intervals, hypothesis testing, regression, and experiment design.
• Practical understanding of supervised learning, validation, overfitting, feature engineering, and model evaluation.
• Data visualization and the ability to choose charts that match the decision being made.
• Basic understanding of databases, APIs, version control, and data privacy.

Soft Skills

Communication is often the differentiator among candidates with similar technical backgrounds. Hiring teams want someone who can translate an ambiguous question into a measurable problem, explain tradeoffs without unnecessary jargon, and state what the data cannot prove.

Curiosity, organization, and intellectual honesty are equally important. An entry-level analyst who identifies a flawed metric, asks a precise question, and proposes a practical next step is more valuable than one who produces a polished but unreliable model.

Experience Expectations

A bachelor’s degree in statistics, mathematics, computer science, economics, engineering, or a related discipline may be preferred, but requirements differ by employer. A graduate degree can help for research-heavy positions, yet many entry-level roles are accessible through strong coursework, internships, boot camps, or demonstrable independent projects.

GPA is usually less persuasive than evidence of applied ability. A portfolio should include two or three complete projects with a clear question, documented data preparation, appropriate methodology, visual results, and a conclusion tied to a business or social outcome.

Salary and Benefits

Compensation depends heavily on employer, location, industry, and experience. Built In listings show that remote data science compensation can vary widely; examples include a remote data scientist listing offering $135,000 to $170,000 annually, while senior postings may exceed $200,000. Those figures should not be treated as entry-level benchmarks.

A realistic planning range for a U.S. entry-level data science role is approximately $70,000 to $115,000 in base salary, with higher ranges possible in major technology markets or specialized industries. Total compensation may include bonuses, equity, health insurance, retirement contributions, paid time off, professional development funds, and home-office support.

Remote benefits vary significantly. Some employers provide equipment, coworking reimbursement, internet assistance, or recurring work-from-home allowances; others simply permit remote work within approved states. Full-time conversion is possible after an internship or contract assignment, but it depends on performance, budget, business demand, and the availability of an appropriate opening.

Built In Hiring Process

Step-by-Step Hiring Stages

1. Application: Submit a targeted resume, portfolio, and any requested work samples through the individual listing.
2. Screening: A recruiter or hiring coordinator verifies eligibility, location, compensation expectations, and baseline qualifications.
3. Assignment: Some employers use a SQL exercise, take-home analysis, notebook review, or timed technical assessment.
4. Interviews: Candidates may meet with a recruiter, data scientist, hiring manager, and cross-functional partners.
5. Offer: Finalists receive compensation details, work-location requirements, benefits information, and onboarding expectations.

Application Timeline

Because Built In hosts listings from different employers, there is no universal hiring timeline. A straightforward entry-level process may take two to six weeks, while larger organizations can take longer because of multiple interview panels or approval steps. Apply early and continue monitoring the listing for status changes.

Screening Methods

Applicant tracking systems commonly search for role-specific terms such as SQL, Python, experimentation, forecasting, machine learning, Tableau, and statistical modeling. Use the language of the listing naturally in your resume, but connect each skill to an accomplishment rather than creating a dense keyword block.

Portfolio links should open without a login and should be easy to review on a phone. A recruiter should understand the project’s purpose, your contribution, the method used, and the result within a few minutes.

Interview Preparation

Example Interview Questions

• How would you investigate a sudden decline in a company’s weekly active users?
• Write a SQL query to identify the highest-value customers by month and explain your assumptions.
• Describe a project where the data was incomplete, inconsistent, or biased. What did you do?
• How would you evaluate whether a recommendation or machine learning model is successful?

How to Answer

Use the STAR method for behavioral questions: explain the situation, define the task, describe your actions, and quantify the result. For technical questions, begin by clarifying the business objective, then state assumptions, outline the method, discuss tradeoffs, and explain how you would validate the result.

Do not rush into algorithms. Interviewers often care more about problem framing, metric selection, data quality, and interpretation than about selecting the most advanced model. A simple, explainable baseline can be the strongest answer when it is aligned with the decision at hand.

What Recruiters Evaluate

Recruiters look for relevant experience, clear communication, evidence of learning, and realistic expectations about entry-level work. Hiring managers evaluate technical fundamentals, judgment, attention to detail, and whether the candidate can collaborate remotely without constant supervision.

How to Get Selected

Practical Tips

• Customize the top third of your resume for every listing.
• Lead with measurable outcomes, such as reduced processing time or improved forecast accuracy.
• Practice SQL under time limits and explain every query aloud.
• Prepare a two-minute explanation of each portfolio project.
• Research the employer’s product, customers, revenue model, and likely data problems.
• Ask interviewers how the team defines success during the first six months.

Common Mistakes to Avoid

• Submitting a generic resume that does not match the listing’s terminology.
• Presenting a model without explaining the business decision it supports.
• Ignoring data leakage, missing values, sampling bias, or model limitations.
• Overstating experience with tools that were used only briefly.
• Failing to confirm remote-work eligibility, time-zone expectations, or location restrictions.

How to Stand Out

Build one portfolio project that resembles the employer’s actual work. For a marketplace company, analyze conversion and retention; for healthcare, address privacy and class imbalance; for financial technology, discuss risk, false positives, and explainability. A focused project creates a stronger signal than several unfinished tutorials.

Networking can also improve visibility. Reach out to employees with a short, specific message about a published project or product question rather than asking immediately for a referral. If someone refers you, make sure your application still demonstrates independent preparation.

Final Thoughts

Best Remote Entry Level Data Science Jobs on Built In can open doors to a broad range of early-career opportunities, but the platform itself is only the beginning. The candidates who advance typically combine SQL and statistical fluency with clear writing, practical projects, and a strong understanding of how data supports business decisions.

Review current listings, identify the recurring requirements, and build your application around evidence rather than adjectives. That is the most reliable path toward getting hired at Built In-listed employers and turning a remote data science opportunity into a durable career.

Frequently Asked Questions

What Is The Salary?

There is no single salary because Built In lists positions from many employers. A reasonable U.S. entry-level planning range is approximately $70,000 to $115,000 in base pay, while specialized or higher-cost-market roles may offer more. Individual postings remain the best source for exact compensation.

How Competitive Is Hiring?

Competition is strong because remote roles attract applicants from a wide geographic area. Candidates improve their odds by applying early, meeting the core technical requirements, showing relevant projects, and communicating clearly about their impact.

Which Skills Matter Most?

SQL, Python, statistics, data visualization, and business communication are the most broadly useful skills. Employers also value problem framing, data-quality judgment, documentation, and the ability to explain technical findings to nontechnical teammates.