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About This Role
Headquartered in Birmingham, Alabama, Moultrie (www.moultrie.com) is the leader in game feeders and cellular camera innovation, building products used by hunters, property owners, and others for real\-time remote monitoring.
We take pride in developing deep user understanding, obsessing about the details, and going the extra mile to show our users we love them. Moultrie is customer\-driven – hardware, software, marketing, and customer success teams collaborate to deliver a quality user experience.
We are guided by the following principles: Customer Obsession.; Excellence is the Standard.; Bias for Action.; Act Boldly.; Deliver Results.; Hire and Develop the Best.; Be Curious and Learn.; Win as a Team
Job Summary
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Moultrie is looking for a Data Scientist to join the growing Data and Analytics Team. This team owns the development of insights from extraction that power decision\-making across Moultrie. This role will build the predictive and prescriptive modeling capabilities that sit on top of the foundational data layer to surface actionable insights and recommendations within BI products and downstream systems. You will own the statistical modeling and feature engineering that exists in the Data
- *Data Layer \& Feature Store: Snowflake*
- *Model Deployment: Snowflake ML Functions, Snowpark*
- *Development: Python, SQL*
- *Feature store: Snowflake*
- *Experiment Tracking: MLflow*
Typical work includes building and maintaining feature stores Snowflake/dbt, training and validating predictive models against curated datasets, and delivering model outputs as attributes made available in datasets ready for downstream tools.
The ideal candidate has hands\-on experience with applied predictive modeling in a business context (churn prediction, customer health scores, propensity scoring) and can contribute to the data work required to support it. This role will work closely with the other members of the Data and Analytics team as well as business stakeholders to align work with highest business impact.
Job Responsibilities
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Predictive and Prescriptive Modeling
- Design, build, and maintain predictive models that address defined business questions (customer churn, subscription health, purchase propensity) using data from the foundational layer in Snowflake.
- Deliver model outputs as attributes that can integrate cleanly into downstream BI products (Tableau, Streamlit) and activation platforms (BlueConic, Braze, TripleWhale).
- Work with business stakeholders to translate ambiguous questions into scoped modeling problems with defined success metrics.
- Communicate model outputs and their business implications clearly to non\-technical audiences.
- Track, document, and monitor experiments and deployed models to ensure outputs are reliable, understandable, and reproduceable.
Feature Store and Data Engineering
- Contribute to the build and maintenance of features in the feature store: defining features, documenting refresh cadence, and ensuring feature pipelines are reliable and tested.
- Build and maintain training datasets with clear documentation of assumptions, evaluation windows, and limitations.
- Work with data engineers to ensure data models are structured to support feature engineering and model training.
- Ability to apply data engineering fundamentals (SQL modelling, versional control, documentation) to contribute to the feature store and build statistical models that integrate into the existing foundational tech stack.
Job Requirements
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Skills and Qualifications
- 4\+ years of hands\-on experience in data science or a role with significant applied modelling.
- Demonstrated experience building and deploying predictive models in a business context.
- Strong Python proficiency: Proven experience using libraries (scikit\-learn, pandas) to build and maintain predictive models.
- Experience with classification and regression techniques and the ability to validate model performance using appropriate metrics (precision, recall, AUC, etc.).
- SQL proficiency: Able to write clean, maintainable code for supporting models in Snowflake with support from data engineers.
- Experience with Git and standard software development practices: version control, code reviews, branching, and CI/CD basics.
- Ability to take an ambiguous business problem and work backwards to produce models that support effective solutions by creating a list of requirements and working through sprints to deliver.
- Strong documentation practices: able to produce and maintain model definitions, lineage documentation, and data dictionaries that enable other developers and business stakeholders.
- Collaborative working style: comfortable operating at the boundary between data engineering, data science, and business teams.
Preferred Qualifications
- Experience in retail, CPG, or consumer hardware.
- Hands\-on experience with a feature store platform.
- Experience with MLflow or a comparable experiment tracking tool.
- Familiarity with Snowpark or Snowflake ML Functions.
- Experience delivery model outputs that can be used in BI tools and other downstream platforms.
Essential Job Function
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We are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.
We comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.
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 PRADCO, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
PRADCO AI Hiring
PRADCO has 1 open AI role right now. They're hiring across Data Scientist. Based in Birmingham, AL, US.
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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