Data & Analytics (D&A) Developer

About the role

We are seeking a curious, analytically sharp, and digitally passionate Data Scientist to join our HDPE Operations & Strategy team. This role offers the opportunity to create meaningful impact by supporting engineering, operations, business planning, and digital transformation initiatives through data intelligence and AI-enabled solutions. The successful candidate will serve as a bridge between engineering domain knowledge, business operations, and IT execution teams, helping define data requirements, optimize reporting, and develop predictive models that support strategic decision-making across the company’s global business.

Responsibilities

• Analyze data from multiple enterprise systems, including SAP, Salesforce, Databricks, Power BI, labor systems, and finance platforms to identify patterns, data quality issues, and opportunities for improvement.
• Partner with Program Managers and Operations leaders to define relevant data assets, data access requirements, and business use cases.
• Transform large structured and unstructured datasets into actionable business insights.
• Perform data validation, quality checks, and resolution of data anomalies across enterprise platforms.
• Develop and validate machine learning models that support demand forecasting, predictive analytics, and scenario planning initiatives.
• Document analytical methodologies, model performance results, and data definitions to ensure transparency and reproducibility.
• Collaborate with Data Engineers to support implementation of data requirements and maintain Python-based data pipelines for ETL processes, model training, and forecasting workflows.
• Translate business and operational challenges into clearly defined data science and AI/ML problem statements.
• Leverage Large Language Models (LLMs) and prompt engineering techniques to support intelligent tools and workflow automation.
• Design and execute scenario planning models to evaluate business assumptions related to demand forecasts, resource capacity, budgets, and timelines.
• Analyze project execution data from Primavera P6 and other project management systems to assess performance against planning assumptions.
• Conduct variance analysis between forecasted and actual project outcomes, identifying trends, risks, and root causes.
• Develop automated solutions that track project performance and assumption validity throughout project lifecycle stages.
• Support executive dashboard development by providing data pipelines and analytics that visualize project performance and business KPIs.
• Review existing dashboards, reports, models, and data pipelines to understand business logic, data flows, and reporting requirements.
• Interpret complex SQL queries and semantic data models to support ongoing reporting and analytics enhancements.
• Identify opportunities to optimize, consolidate, and improve reporting and modeling assets while maintaining organizational standards.
• Communicate technical findings and model outputs to both technical and non-technical stakeholders in a clear and actionable manner.
• Support internal user inquiries related to reporting, analytics, data quality, and model outputs.
• Collaborate with cross-functional teams to ensure data requirements are properly understood and implemented.
• Stay current with advancements in AI, machine learning, and data science, contributing innovative ideas for continuous improvement.

Requirements

Education

• Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field.

Experience

• Experience in data science, analytics, predictive modeling, or a related discipline.
• Experience working with enterprise datasets and multiple business systems.
• Experience developing machine learning models and analytical solutions.
• Experience collaborating with Data Engineers, business stakeholders, and cross-functional teams.
• Experience working with project execution, operational, or business planning data is preferred.

Skills

• Strong proficiency in Python, including pandas, numpy, scikit-learn, scipy, curve fitting, and object-oriented programming.
• Experience building scenario planning and what-if analysis models.
• Foundational to intermediate machine learning knowledge using frameworks such as scikit-learn, XGBoost, or similar tools.
• Understanding of model evaluation techniques including R², MAE, RMSE, cross-validation, and custom scoring methods.
• Proficiency in SQL for querying, joining, and manipulating data.
• Knowledge of statistical modeling, hypothesis testing, and experimental design.
• Ability to independently explore enterprise datasets and identify meaningful insights.
• Experience cleaning, standardizing, and integrating data from multiple sources.
• Strong understanding of anomaly detection and data quality processes.
• Knowledge of semantic data modeling concepts.
• Experience with forecasting, predictive analytics, and business modeling.
• Familiarity with Large Language Models (LLMs) and prompt engineering techniques.
• Ability to review dashboards, reports, SQL queries, and data assets to understand business logic and data lineage.
• Strong communication, problem-solving, and stakeholder engagement skills.
• Ability to explain complex technical concepts to non-technical audiences.
• Strong analytical thinking, attention to detail, and learning agility.
• Professional English communication skills, written and verbal.

Preferred Skills

• Experience with TensorFlow, PyTorch, neural networks, or other deep learning technologies.
• Knowledge of unit testing frameworks such as pytest.
• Experience with Primavera P6, Microsoft Project, or similar project management systems.
• Familiarity with MLOps concepts including model versioning and experiment tracking tools such as MLflow or Weights & Biases.
• Experience working with cloud platforms including Azure, AWS, or GCP.
• Knowledge of advanced LLM applications, including retrieval-augmented generation (RAG), fine-tuning, or agent frameworks.
• Understanding of data governance and responsible AI practices.
• Experience working with enterprise platforms such as SAP, Salesforce, Databricks, ERP, or CRM systems.
• Ability to work effectively across international, multicultural teams and evolving business environments.

Pay

The typical base pay for this role across the U.S. is $53.08/hr. Non-exempt positions are eligible for overtime at a rate of 1.5 times the base hourly rate for all hours worked in excess of 40 in a work week, or as required by state or local law.

Benefits

• Full-time employees are eligible to select from different benefits packages, which may include:
  • Medical, dental, and vision benefits
  • Health savings accounts with qualified medical plan enrollment
  • 10 paid days off
  • 3 days paid bereavement leave
  • 401(k) plan participation with employer match
  • Life and disability insurance
  • Commuter benefits
  • Dependent care flexible spending account
  • Accident insurance
  • Critical illness insurance
  • Hospital indemnity insurance
  • Accommodations and reimbursement for work travel
  • Discretionary performance or recognition bonus
• Sick leave and mobile phone reimbursement provided based on state or local law.