What are the responsibilities and job description for the Data & Analytics (D&A) Developer position at Aditi Consulting?
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Summary
Company is accelerating the path to more reliable, affordable, and sustainable energy while helping customers power economies and deliver electricity that is vital to health, safety, security, and quality of life.
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 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.
Education Requirements
- Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field.
- 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.
- 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.
- 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.
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Salary : $53