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About This Role
Position Title
Data Scientist SrLocation
Atlanta, GA 30305Job Summary
The Data Scientist Sr responsibilities include independently perform data analytics (ranging from data analysis, regression to machine learning) in a variety of environments using structured and unstructured data. Present complex concepts in a format digestible by a diverse audience across the organization. Build and improve the development of metrics, reports and analysis into BI dashboards that support a wide array of banking businesses. Quickly learn complex community banking dynamics, work closely with stakeholder to understand business objectives and design data\-driven solutions that create competitive differentiation. Partner across the functions, including the line of businesses, IT, Data Office, and/or third\-party vendors to co\-build the data foundation and analytics delivery infrastructure for the action\-driven insights and reporting.Job Responsibilities:
JOB RESPONSIBILITIES
- Independently perform data analytics (ranging from data analysis, regression to machine learning) also guide other data scientists properly in a variety of environments using structured and unstructured data.
- Present complex concepts in a format digestible by a diverse audience across the organization.
- Build and improve the development of metrics, reports and analysis into BI dashboards that support a wide array of banking businesses.
- Manage ongoing data analytics processes, document calculations, code, and adhere to proper risk and compliance practices.
- Trouble shoot issues, suggest alternative approaches or controls to mitigate risks/gaps identified.
- Develop and design data\-driven solutions in the areas of client insights and segmentation, sales \& marketing lead generation, performance analysis and product analytics.
- Partner across the functions, including the line of businesses, IT, Data Office, and/or third\-party vendors to co\-build the data foundation and analytics delivery infrastructure for the action\-driven insights and reporting.
- Assume end\-to\-end responsibilities on key initiatives assigned, and leverage partnership to achieve the shared vision.
- Uses independent judgement and discretion to make decisions.
- Analyzes and resolves problems.
ADDITIONAL ACCOUNTABILITIES
- Performs special projects, and additional duties and responsibilities as required.
- Consistently adheres to regulatory and compliance policies and standards linked to the job as listed and complete required compliance trainings. Accountable to maintain compliance with applicable federal, state and local laws and regulations.
JOB REQUIREMENTS
Required Qualifications:
- Education level required: Undergraduate Degree (4 years or equivalent).
- Minimum experience required: 5\+ Years relevant data engineering, data analytics, computer science, business intelligence industry experience.
- Experience leveraging tools for ETL and data wrangling.
- Experience programming statistical \& data science languages (e.g. Excel VBA, SQL, Python, R).
- Experience using modern visualization tool (e.g. PowerBI, Tableau).
- Knowledge of advanced analytics (e.g. Statistical modeling, machine learning, text analytics) and big data environments.
Preferred Qualifications:
- Education level preferred: Master's Degree (or Postgraduate equivalent) includes a Master of Business Administration (MBA) with a focus on data analytics or a Master of Science (MS) in data science, business analytics, computer engineering, etc.
- Working knowledge of cloud computing tools on any of the major cloud platforms preferred.
- Completion or progress towards certifications such as FRM or CFA preferred.
- Experience with ETL tools such as Informatica, AWS, Talend, or Snowpark.
- Knowledge of data governance, security, and compliance requirements in the banking industry.
Job Competencies:
- Knowledge of advanced analytics (e.g. Statistical modeling, machine learning, text analytics) and big data environments.
- Knowledge of retail and/or commercial banking.
- Good Interpersonal, communication and presentation skills.
- Results oriented with a strong work ethic and sense of urgency.
- Demonstrates a strong ability to build and maintain effective relationships with stakeholders by communicating clearly, engaging in proactive collaboration, and leveraging cross functional insights.
- Aligns relationship building efforts with enterprise goals to accelerate performance and drive strategic results.
- Builds trusted client relationships, whether internal or external, by identifying needs and delivering tailored solutions to enhance the overall client experience.
- Fosters or supports a positive work culture and productive work environment, displaying importance of effective relationships with customers and stakeholders.
- Physical demands (ADA): No unusual physical exertion is involved.
Flagstar is an Equal Opportunity Employer
We are committed to providing clear and accurate compensation information in accordance with applicable laws. Actual starting base pay will be determined based on location, experience, and other non\-discriminatory factors permitted by law. Total compensation may also include variable incentives, bonuses, commissions, or other awards as outlined in the offer of employment. Flagstar provides teammates access to a variety of benefits including medical, dental, vision, life, and disability insurance, as well as a comprehensive leave program. Please click the following link for detailed information: *Benefits \| Flagstar Bank*
Pay Range
$85,218\.75 \- $140,390\.00Qualified applicants with arrest or conviction records will be considered for employment in accordance with the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, and the San Francisco Fair Chance Ordinance, as appliable.
Salary Context
This $85K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At FlagStar Bank, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($112K) sits 43% below the category median. Disclosed range: $85K to $140K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
FlagStar Bank AI Hiring
FlagStar Bank has 1 open AI role right now. They're hiring across Data Scientist. Based in Atlanta, GA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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