Data Scientist (Data & Analytics)

Halian | Managed Services, Recruitment Agency & Contract Staffing; South Carolina; On-site; USD 110,000 - 140,000; Full time

Job summary

Halian | Managed Services, Recruitment Agency & Contract Staffing is seeking a Data Scientist to apply data science, ML, and predictive analytics to improve operational planning, forecasting, and decision-making across engineering, operations, business, and data teams.

The ideal candidate will have strong Python and SQL skills, hands-on experience building predictive models, and the ability to translate analytical findings into practical business recommendations that influence planning and

Qualifications

• Strong Python programming with pandas, NumPy, SciPy, and scikit-learn.
• SQL proficiency for data extraction, transformation, and analysis.
• Hands-on experience with machine learning and predictive modeling.
• Experience with model validation techniques (R², MAE, RMSE, cross-validation).
• Experience with forecasting and scenario analysis.
• Ability to clean, transform, and integrate data from multiple sources.
• Strong analytical and problem-solving skills with translating business needs to data solutions.
• Effective communicator with ability to present findings to technical and non-technical stakeholders.

Responsibilities

• Analyze and integrate data from enterprise systems (SAP, Salesforce, Databricks, Power BI).
• Develop, test, and validate ML models for forecasting and scenario planning.
• Build what-if analyses to evaluate assumptions, capacity, demand, costs, and outcomes.
• Use Python to develop data preparation, ETL, model training, forecasting workflows.
• Apply statistical analysis, hypothesis testing, and experimental design.
• Evaluate model performance using R², MAE, RMSE, and cross-validation.
• Investigate dashboards, SQL queries, and models to understand data lineage.
• Collaborate with data engineers to improve data pipelines and quality.
• Analyze planned vs actual performance (labor hours, budgets, timelines).
• Communicate findings through dashboards, reports, and presentations for stakeholders.
• Explore large language models and prompt engineering for business applications.

Skills

Python

SQL

Machine learning

Statistical analysis

Pandas

NumPy

scikit-learn

Data wrangling

Model validation

Experiment design

Communication

Tools

TensorFlow

PyTorch

MLflow

Weights & Biases

AWS

Azure

Google Cloud

Databricks

Primavera P6

Microsoft Project

Job description

We are seeking a Data Scientist to apply data science, machine learning, and predictive analytics to improve operational planning, forecasting, and business decision-making. This role will work across engineering, operations, business, and data teams to transform complex enterprise data into actionable insights and develop models that support scenario planning and performance optimization.

The ideal candidate has strong Python and SQL skills, hands-on experience developing predictive models, and the ability to translate analytical findings into practical business recommendations.

Key Responsibilities

• Analyze and integrate data from enterprise systems, including SAP, Salesforce, Databricks, Power BI, and other operational and financial sources.
• Develop, test, and validate machine learning models for forecasting, prediction, and scenario planning.
• Build what-if analyses to evaluate business assumptions, resource capacity, demand forecasts, costs, and project outcomes.
• Use Python to develop data preparation, ETL, model training, and forecasting workflows.
• Apply statistical analysis, hypothesis testing, and experimental design to support data-driven decisions.
• Evaluate model performance using appropriate metrics, including R², MAE, RMSE, and cross-validation.
• Investigate existing dashboards, reports, SQL queries, and analytical models to understand data lineage and business logic.
• Collaborate with data engineers to improve data pipelines, data quality, and analytical workflows.
• Analyze planned-versus-actual performance, including labor hours, budgets, timelines, and operational results.
• Communicate findings through dashboards, reports, and presentations for technical and business stakeholders.
• Explore the use of large language models (LLMs) and prompt engineering for business and operational applications.

Required Qualifications

• Strong programming skills in Python, including experience with libraries such as pandas, NumPy, SciPy, and scikit-learn.
• Experience with SQL for data extraction, transformation, analysis, and investigation of complex datasets.
• Hands-on experience with machine learning, predictive modeling, and statistical analysis.
• Understanding of model validation and evaluation techniques, including R², MAE, RMSE, and cross-validation.
• Experience with forecasting, scenario analysis, what-if modeling, or predictive analytics.
• Ability to clean, transform, and integrate data from multiple sources.
• Strong analytical and problem-solving skills, with the ability to translate business needs into data-driven solutions.
• Effective communication skills and experience presenting analytical findings to technical and non-technical stakeholders.

Preferred Qualifications

• Experience with enterprise platforms such as SAP, Salesforce, Databricks, or ERP/CRM systems.
• Familiarity with project planning and scheduling tools such as Primavera P6 or Microsoft Project.
• Exposure to LLMs, prompt engineering, retrieval-augmented generation (RAG), or AI-powered business applications.
• Experience with machine learning frameworks such as TensorFlow or PyTorch.
• Familiarity with MLOps tools such as MLflow or Weights & Biases.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Experience with data governance, model monitoring, automated testing, or production ML workflows.

What Makes This Opportunity Interesting?

This role combines hands-on data science with real-world operational and business challenges. You will help develop predictive models, evaluate alternative scenarios, improve planning processes, and turn complex enterprise data into insights that support better decisions across engineering and operations.