Data Scientist In Finance

Data Scientist in Finance at Built In job listing — Research Report

Introduction

The Data Scientist in Finance role at Built In job listing sits at the intersection of analytics, business strategy, and financial decision-making. For candidates who want a remote, high-impact role that builds a strong foundation in data science while staying close to real business outcomes, this is the kind of position that can accelerate a career fast. The internship/job period is listed with a deadline that is not mentioned, so applicants should move quickly and prepare polished materials before the posting changes.

This role is especially valuable because finance-focused data scientists often work on questions that directly affect revenue, forecasting, risk, pricing, and customer behavior. That makes the experience highly transferable across fintech, banking, consulting, SaaS, and corporate strategy. If you are trying to understand how to get hired at Built In job listing, this report breaks down the company, the job, the likely hiring process, and the skills that matter most.

Overview of Built In job listing

Built In is best known as a digital platform for tech professionals, employer branding, and job discovery. It helps companies reach candidates through city-based tech communities, editorial content, salary data, and job listings, while also serving as a talent marketing channel for employers. In practice, that means its business sits at the overlap of recruiting media, local tech ecosystems, and employer branding technology.

Its competitors and adjacent players include major job boards, niche tech job sites, recruiting platforms, and employer branding networks. Built In’s niche is tighter than general-purpose boards because it emphasizes tech talent, local markets, and content that helps candidates understand companies more deeply. That positioning matters in a crowded hiring landscape where candidates want more than just a job title and a pay range.

Built In’s key products and services typically include job listings, employer profiles, salary information, editorial coverage, community content, and recruiting solutions for companies trying to attract technical talent. The platform’s market presence is strongest among technology-minded job seekers and employers who want visibility in cities with active startup and tech ecosystems. Its reputation is closely tied to being useful, searchable, and candidate-friendly.

People want to work through or with Built In because the platform sits near the center of tech hiring trends. For job seekers, that means access to roles that are often remote, early-career friendly, and tied to modern skill sets like data, analytics, product, and finance. For employers, it offers a way to tell a more credible story about the team, culture, and role expectations.

Data Scientist in Finance Role

Role Overview

A Data Scientist in Finance typically uses statistical modeling, Python, SQL, and business analysis to solve problems tied to money, performance, and risk. In a remote setting, the work usually centers on extracting data, cleaning it, analyzing patterns, and turning those findings into decisions that non-technical stakeholders can act on. Depending on the team, the role may support forecasting, budgeting, fraud detection, pricing strategy, portfolio analysis, or customer segmentation.

Day to day, this person is not just building models in isolation. The real value comes from translating findings into practical recommendations that influence product strategy, financial planning, or operational efficiency. In finance-heavy environments, a good data scientist can shape how a company spends, grows, and protects margin.

Detailed Responsibilities

• Analyze structured financial and operational data to identify trends, anomalies, and opportunities.
• Build and validate predictive models for forecasting, risk, or business performance.
• Write SQL queries to pull data from warehouses and reporting systems.
• Clean, transform, and document datasets for repeatable analysis.
• Create dashboards, charts, and reports for finance and business stakeholders.
• Support ad hoc analysis for pricing, revenue, expense, or customer behavior questions.
• Work with product, finance, and operations teams to define metrics and business logic.
• Communicate findings clearly in written summaries and presentations.

Day-to-Day Workflow

A realistic day often starts with checking project priorities, validating data refreshes, and reviewing any broken pipelines or inconsistent metrics. After that, the data scientist may spend a few hours in SQL and Python pulling datasets, testing assumptions, and comparing results against prior reporting cycles. If a model or analysis is in progress, the middle of the day often goes into feature engineering, experimentation, or error analysis.

Later in the day, meetings with finance or product partners may be used to clarify a business question, review findings, or align on next steps. Remote finance analytics roles tend to reward strong documentation habits because teams need to understand not just the answer, but how the answer was produced. A candidate who can explain tradeoffs, data quality issues, and business implications will usually outperform someone who only shows technical output.

Tools and Technologies

Most roles like this rely on a familiar analytics stack rather than highly specialized tooling. Expect heavy use of Python, SQL, Excel, and either Tableau, Power BI, or another visualization layer. Depending on the company, you may also see Jupyter notebooks, dbt, Git, Snowflake, BigQuery, or internal data platforms.

For finance-specific work, familiarity with forecasting methods, time-series analysis, regression, A/B testing, and metric design is often more important than cutting-edge machine learning. If the team is mature, they may also expect experience with dashboarding, data validation, and reproducible analysis workflows.

Skills and Requirements

Technical Skills

The strongest candidates usually combine data science fundamentals with enough finance fluency to ask smart questions. That means being comfortable with SQL joins, pandas, NumPy, basic statistics, hypothesis testing, and visualization best practices. A candidate who understands concepts like revenue recognition, unit economics, margin, churn, or forecasting error has a noticeable advantage.

Modeling skills matter, but practical interpretation matters just as much. In many finance roles, the team is looking for someone who can build a clean baseline model, explain why it works, and identify where it may fail. Familiarity with dashboards, ETL concepts, and data quality checks is also highly valuable.

Soft Skills

Communication is critical because finance data science work often needs to persuade non-technical stakeholders. You will likely need to explain assumptions, defend methodology, and make your results understandable to people who care about business outcomes more than model architecture. Strong collaboration skills also matter because the work often spans finance, product, engineering, and operations.

Problem-solving and intellectual curiosity are especially important in remote roles. Hiring teams want evidence that you can work through ambiguity, ask clarifying questions, and stay organized without constant supervision. Candidates who document their reasoning well usually come across as more mature and dependable.

Experience Expectations

For an internship or early-career role, a polished project portfolio can matter as much as formal work experience. Expect the hiring team to look for coursework, side projects, internships, research work, or competition results that show real analytical ability. If the role is aimed at students or recent graduates, a strong GPA can help, but it rarely compensates for weak applied experience.

Useful portfolio pieces include forecasting projects, risk modeling notebooks, pricing analyses, SQL case studies, or dashboards built around public financial data. If you do not have direct finance experience, translating a general data project into a business story is the next best thing.

Salary and Benefits

Because the posting does not list compensation, the most realistic market estimate depends on whether this is an internship or a junior full-time role. For a remote Data Scientist in Finance internship in the U.S., a fair range is often around $30 to $55 per hour, with top-paying tech or finance teams sometimes going higher. For a full-time entry-level role, many remote U.S. employers pay roughly $85,000 to $125,000 annually, with finance-heavy teams potentially paying more depending on location, scope, and experience.

Typical benefits in remote analytics roles can include flexible work scheduling, home office support, access to learning platforms, mentorship, and performance-based raises or bonuses. If the internship is structured well, a strong performer may also have a path to full-time conversion, especially if the team needs help building analytics capacity. For candidates, that conversion possibility can be just as valuable as the immediate stipend or salary.

Built In job listing Hiring Process

Step-by-Step Hiring Stages

1. Application
2. Screening
3. Assignment
4. Interviews
5. Offer

The application stage usually filters for relevant keywords, project experience, and evidence of analytical rigor. Screening often includes a recruiter conversation about interest, availability, remote setup, and baseline technical fit. The assignment stage, if used, may involve SQL, Python, forecasting, dashboarding, or business-case analysis.

Application Timeline

Because the deadline is not mentioned, the safest strategy is to apply immediately rather than waiting for a formal closing date. Many remote analytics roles start review on a rolling basis, and strong candidates can move through the process in one to three weeks if the team is actively hiring. If the posting is high priority, delays often happen only when interview calendars fill up.

Screening Methods

Screeners usually look for ATS-friendly keywords such as Python, SQL, statistics, forecasting, machine learning, finance, dashboards, and remote collaboration. They may also scan for portfolio links, GitHub, Kaggle, Tableau Public, or project writeups that prove you have used these tools in real settings. Clear, concise resumes generally outperform dense ones because they make your impact easy to evaluate fast.

Interview Preparation

Example Interview Questions

• How would you forecast monthly revenue using incomplete historical data?
• Describe a time you cleaned a messy dataset and what tradeoffs you made.
• How would you measure whether a financial model is performing well?
• Tell us about a project where your analysis changed a decision.

How to Answer

Use the STAR method: situation, task, action, result. For data science roles, make sure the “action” section includes your technical approach, not just the business context. Strong answers usually name the tools used, the assumptions tested, the metric improved, and the business implication of the result.

When discussing modeling or analysis, explain why you chose a method instead of listing methods you know. Interviewers want to see judgment, not just vocabulary. If your work had limitations, acknowledge them directly and explain how you would improve the analysis next time.

What Recruiters Evaluate

Recruiters generally evaluate technical fundamentals, finance awareness, communication quality, and evidence that you can work independently. They also pay attention to whether you can tell a coherent story about your background. If your resume feels random, the team may assume your thinking is random too.

For this role, hiring managers also care about business judgment. They want someone who can connect analysis to revenue, cost, risk, or customer outcomes without needing every question translated into plain English.

How to Get Selected

Practical Tips

Tailor your resume to the role by putting SQL, Python, forecasting, and finance-related project work near the top. Replace vague bullets with measurable results such as accuracy improvements, time saved, dataset size, or decision outcomes. If you have not worked in finance directly, make your coursework and side projects look like financial analysis work by using real-world data and business framing.

Prepare a short portfolio with two or three strong examples rather than a long list of weak ones. A simple GitHub repo with clean notebooks, a one-page project summary, and a dashboard screenshot can be more persuasive than a crowded resume. If the role is remote, mention your ability to work asynchronously and document work clearly.

Common Mistakes to Avoid

One common mistake is overemphasizing advanced machine learning when the role really needs practical analysis and business interpretation. Another is submitting a resume that lists tools without showing outcomes, which makes it hard for recruiters to understand your impact. Candidates also get rejected when they ignore finance context and speak only in generic data-science language.

Weak communication is another frequent problem. If you cannot explain a project clearly in two minutes, the interview team may worry about how you will present findings to finance stakeholders. Poorly organized notebooks, broken links, and vague project descriptions also hurt credibility fast.

How to Stand Out

To stand out, build a portfolio project around a finance problem that matters, such as revenue forecasting, credit risk, expense anomaly detection, or customer lifetime value. Use public data, but frame it like a business case. Then publish a short writeup that explains the question, method, result, and limitations in plain English.

Networking can also help. If you can connect with current employees, recruiters, or people who have worked in similar finance analytics roles, you can learn what the team really values and adjust your application accordingly. A tailored submission that reflects the company’s language and priorities usually performs better than a generic one.

Final Thoughts

The Built In job listing internship or job posting for Data Scientist in Finance is the kind of opportunity that can sharpen both technical skill and business judgment. It is especially attractive for candidates who want remote work, real analytical ownership, and exposure to finance problems that matter. If you can combine solid SQL, Python, statistical reasoning, and crisp communication, you will already be ahead of many applicants.

Focus on presenting yourself as someone who can do more than analyze data. Show that you can help a team make better financial decisions. That is the fastest path to getting noticed, moving through interviews, and landing the role.

Frequently Asked Questions

Salary at Built In?

The estimated salary for a Data Scientist in Finance at Built In job listing depends on whether the role is internship or full-time. A realistic U.S. range is about $30 to $55 per hour for internships and roughly $85,000 to $125,000 annually for entry-level full-time roles.

How competitive is it?

It is moderately to highly competitive because finance and data science are both in demand, and remote roles attract broad applicant pools. Candidates with strong project portfolios, finance knowledge, and clear communication have a much better chance of getting shortlisted.

Most important skills?

The most important skills are Python, SQL, statistics, forecasting, data cleaning, and the ability to explain financial insights clearly. Finance intuition and strong stakeholder communication are just as important as technical ability.