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About This Role
CLIENT HIGHLIGHT
The client you will be working for is a Fortune 500 Energy and Industrial Technology Company. This opportunity will give you experience in the energy transition sector with a company that is a globally recognized leader in power generation, wind energy, electrification, and grid solutions — and one of the most storied industrial brands in the world.
LOCATION
Greenville, South Carolina, 29615-4614
COMPENSATION
$46.00–$53.00 per hour
SCHEDULE
Monday-Friday
Hybrid – few days in office
CONTRACT TERM
1 year with high likelihood of extension or conversion to full-time employee.
POSITION OVERVIEW – Data & Analytics Developer II - Data Scientist
You’ll use data and AI to help the team plan better and spot problems early. You’ll work between the engineers, the business team, and the IT team, figuring out what data is needed and how to use it. You’ll build models that ask, “what if our plan changes?” and track real project progress in tools like P6 to see where things are drifting off plan. Then you’ll turn the results into clear reports that leaders can use to make decisions.
RESPONSIBILITIES
- Analyze data from enterprise systems (SAP, Salesforce, Databricks, Power BI) to find patterns, gaps, and ways to improve
- Work with Program Managers and Operations leaders to decide what data is needed and how it should be used
- Clean and check large datasets (often 100k+ rows) and fix data defects across platforms
- Build and validate machine learning models for demand forecasting, scenario modeling, and prediction
- Build and maintain Python data pipelines for ETL, model training, and automated forecasting, working with Data Engineers
- Build "what-if" scenario planning models to test business assumptions such as demand, resource capacity, and cost
- Track project execution in P6 (Primavera) and other systems, compare plan to actual, and identify gaps, root causes, and trends
- Build automated tracking and data for executive dashboards that flag projects at risk
- Use LLMs and prompt engineering to build tools that automate workflows and support decisions
- Explain findings to technical and non-technical audiences, answer user questions about model outputs and data issues, and document model performance and data definitions
REQUIRED QUALIFICATIONS/SKILLS
- Python (pandas, numpy, scikit-learn, scipy) for data analysis, statistical modeling, and ML
- SQL: querying, joining tables, and reading complex queries
- Foundational to intermediate ML experience (scikit-learn, XGBoost) and model validation metrics (R², MAE, RMSE, cross-validation)
- Experience building scenario planning and forecasting models
- Statistical analysis: modeling, hypothesis testing, and experimental design
- Experience cleaning and merging messy data from enterprise sources (SAP, Salesforce, Databricks, ERP/CRM)
- Familiarity with LLMs and basic prompt engineering
- Ability to read existing dashboards, models, and SQL to understand data flows and business logic
- Clear communicator who can explain technical findings to non-technical audiences; fluent in English
- Analytical, curious, self-motivated, and comfortable working with global teams
Preferred
- P6 (Primavera), MS Project, or similar project execution systems
- Deep learning (TensorFlow, PyTorch) or advanced LLM work (RAG, fine-tuning, agents)
- MLOps and cloud experience (MLflow, Azure/AWS/GCP)
- Unit testing (pytest) and data governance or responsible AI knowledge
MUST HAVE
- Python (pandas, numpy, scikit-learn) for data analysis, statistical modeling, and ML development
- SQL: querying, joining, and interpreting complex queries
- Scenario planning and forecasting models
- Ability to translate complex data findings into clear business insights for non-technical audiences
Salary Context
This $95K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Wet Coast Logistics, 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 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 46% below the category median. Disclosed range: $95K to $110K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wet Coast Logistics AI Hiring
Wet Coast Logistics has 1 open AI role right now. They're hiring across Data Scientist. Based in Greenville, SC, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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