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About This Role
Overview: About the role As a Senior Data Scientist on Aptean's Strategy team, based in Alpharetta, Georgia, you will use data science and machine learning to answer some of the business's most important strategic questions — from understanding why customers churn and predicting who's at risk, to identifying where we have the strongest opportunities for growth and expansion. You'll partner closely with business leaders, finance, and executives, turning open-ended business questions into rigorous analysis and clear, actionable recommendations.
This role sits at the intersection of technical depth and business fluency: you'll need to be equally comfortable building a predictive model and explaining what it means — and why it matters — to an executive audience.
Key Responsibilities:* Partner with Strategy leadership and business stakeholders to translate ambiguous, open-ended questions into structured analytical approaches
- Mine and analyze data from our unified data platform to answer ad hoc and recurring strategic questions for leadership
- Apply an AI-first approach to problem-solving, leveraging modern AI tools and techniques to accelerate analysis and uncover insights faster
- Build and own predictive models and quantify business impact
- Perform exploratory data analysis, statistical analysis, and A/B testing to validate hypotheses and support decision-making
- Clean, preprocess, and validate large datasets pulled from multiple internal sources
- Build AI-first dashboards and self-service tools so non-technical stakeholders can access and explore insights directly
- Present findings and recommendations clearly to both technical and executive audiences, using visualizations and written reports
About you You're a strong problem-solver with genuine curiosity about the "why" behind the numbers. You're as comfortable presenting to a VP as you are deep in the data itself. You know how to scope a vague business question into something analytically tractable, and you don't wait to be handed a clean dataset and a defined problem — you go find both.
Qualifications:* 5+ years' experience as a Data Scientist, with a track record of predictive modeling projects
- Master's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field required; Ph.D. in a related field strongly preferred
- Strong SQL skills and comfort working directly in large, multi-source data platforms
- Strong understanding of statistics, data modeling, and machine learning algorithms
- Experience with large datasets, visualization tools, and MLOps practices
- Demonstrated ability to present technical findings to non-technical, senior stakeholders — this is a core requirement, not a nice-to-have
To be ahead of the pack, you might have:* Experience with SparkSQL, Fabric or Spark data frameworks
- Familiarity with AWS or Azure for model development and deployment
- Experience with Tableau, Power BI or similar visualization tools
What’s in it for you? Aptean offers competitive pay and robust benefit plans along with the opportunity to grow your career in a fast-paced, flexible and casual environment, an outstanding opportunity for career development and growth. About Aptean At Aptean, our mission is to solve tomorrow’s unique challenges today with unrivaled, purpose-built software and superior customer experiences from people who care. Aptean is a global provider of mission-critical, industry-specific software solutions. Aptean’s purpose-built ERP and supply chain management solutions help address the unique challenges facing process and discrete manufacturers, distributors and other focused organizations. Aptean’s compliance solutions are built for companies serving specific markets such as finance, healthcare, biotech and pharmaceuticals, over 10,000 highly specialized organizations in more than 20 industries and 80 countries rely on Aptean to streamline their everyday operations.
“At Aptean, our global and diverse employee base is our greatest asset. It is through embracing and understanding our differences that we are able to harness our individual power to maximize the success of our customers, our employees and our company.” – TVN Reddy
Aptean pledges to promote a company culture where diversity, equity and inclusion are central. We are committed to applying this principle as we interact with our customers, build our teams, cultivate our leaders and shape a company in which any employee can succeed, regardless of race, color, sex, national origin, sexuality and gender identity, religion, disability or age. Celebrating our diverse experiences, opinions and beliefs allows us to embrace what makes us unique and to use this as an asset in bringing innovative solutions to our customer base.
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Aptean, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Aptean AI Hiring
Aptean has 1 open AI role right now. They're hiring across Data Scientist. Based in Alpharetta, GA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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