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About This Role
About Ameren Services (B\&CS)
Ameren Services provides administrative support and services to Ameren Corporation and its operating companies, subsidiaries and affiliates. Ameren Services includes a wide range of skill sets and roles, from finance and legal experts to digital and cyber specialists, plus those charged with ensuring environmental compliance and operational safety. Together, we help execute a strategy that enables Ameren to deliver superior long\-term value to customers, shareholders and the environment.
Our benefits include:
- Medical coverage on date of hire
- 100% employer paid cash balance pension plan
- 401(k) with company match fully vested on date of hire
- Minimum of 15 days paid vacation and 12 paid holidays
- Paid parental leave and family caregiver leave
Visit our Benefits \& Perks Page for more information on benefits provided to regular full\-time employees.
*About The Position*
- Generates reports and regular datasets or report information for end\-users, using system tools and database or data warehouse queries and scripts. May create specifications for reports based on business requests.
- Designs, develops and programs methods, processes, and systems to consolidate and analyze structured and unstructured, diverse sources to generate actionable insights and solutions for client services and product enhancement. Analyzes and models data to understand complex business issues and provide insight to decision makers. Integrates data from multiple sources to produce requested or required data elements. Programs and maintains reports, information dashboards, data generators, canned reports and other end\-user information portals or resources.
- Uses advanced mathematical and statistical concepts and theories to analyze and collect data; identifies and interprets trends and patterns in datasets
- Supports complex business problems and issues using unstructured data from internal and external sources to provide insight to decision\-makers
- Builds or defines machine learning solutions that create value for the business
*Key responsibilities include:*
- Addresses business problems by applying advanced analytics techniques through broad parameters on big, unstructured data sets
- Designs databases for unstructured data sets, defining how the data will be stored, consumed, integrated and managed
- Develops optimal techniques and problem\-solves to understand and explore multiple reasons for correlations between disparate data sets; provides coaching on how to transform and clean data into an appropriate form for statistical analysis
- Conducts exploratory data analysis from complex data sources and builds key data sets
- Works closely with data analysts and data engineers to review and provide feedback on data pipelines and optimization opportunities
- Translates complex business requirements and technical designs into data science solutions aligned to business goals that deliver measurable value to the organization
- Designs and develops data science solutions that provide new ways of thinking about business problems in alignment with enterprise architecture direction and design standards
- Designs data science models to predict outcomes or impact of key decisions
- Designs and creates reporting and visualization for communicating data models
- Applies latest cutting\-edge research on data science and model development; analyzes and develops recommendations for senior data scientists
- Designs automated, operational analytics processes to achieve scale and durability of analysis processes using Python, R, SAS, or other leading open source statistical tools
- Applies machine learning models to solve business problems with multiple stakeholders
- Develops tests for evaluating algorithms and machine learning system
- Understands the Digital organization’s objectives, and the impact on own projects; contributes to development of new data science strategies
- Rapidly develops proof\-of\-concept prototypes to prove out hypotheses
- Reaches across multiple functions to implement the models into production and monitors their performance
- Demonstrates strong working knowledge of agile software development processes and the development lifecycle; liaises with scrum masters and coaches to move projects forward
- Manages delivery of projects in partnership with business stakeholders by demonstrating a deep understanding of business needs and how technical analysis can produce solutions
- Coaches less experienced co\-workers and provides feedback to enhance skills and knowledge
*Qualifications \- Senior Data Scientist*
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- Bachelor’s degree required, preferably in mathematics, statistics, computer science or business
- 5\+ years of relevant experience
- Consideration will be given to candidates with Advanced Degree with three or more years of relevant experience
- Experience using predictive analytics in a business setting preferred
*Qualifications \- Lead Data Scientist*
- Bachelor’s degree required, preferably in mathematics, statistics, computer science or business
- 7\+ years of relevant experience
- Consideration will be given to candidates with Advanced Degree and five or more years of relevant experience
- Experience using predictive analytics in a business setting preferred
In addition to the above qualifications, the successful candidate will demonstrate:
- Deep knowledge of a scripting or statistical programming language such as Python, R, SAS, or other leading statistical tools with a preference for Python or R
- Advanced SQL
- Databricks experience preferred
- Effectively manage very large datasets
- Advanced ability to work with machine learning data sets
*Additional Information*
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Ameren’s selection process includes a series of interviews and may include a leadership assessment process. Specific details will be provided to qualified candidates.
\#LI\-Hybrid
Compensation Range:
$105,100\.00 \- $204,400\.00* This pay range encompasses multiple levels of the role. Career level and compensation depends upon applicant’s credentials.
At Ameren, base salary is one component of a competitive compensation package for employees. Our pay ranges are broad to allow for movement within our organization and to accommodate different skill sets and levels of expertise. We take into consideration a variety of factors including, but not limited to, skills, abilities, experience, education, credentials, and internal equity when determining the base salary offered. Roles are eligible for additional rewards including annual incentive payments based on individual and company performance.
If end date is listed, the posting will come down at 12:00 am on that date:
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, ethnicity, age, disability, genetic information, military service or status, pregnancy, marital status, sexual orientation, gender identity or expression, or any other class, trait, or status protected by law.
Salary Context
This $105K-$204K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Ameren, 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($154K) sits 20% below the category median. Disclosed range: $105K to $204K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ameren AI Hiring
Ameren has 1 open AI role right now. They're hiring across Data Scientist. Based in St. Louis, MO, US. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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