Description:

About the Job

We are looking for talented individuals who are driven to innovate, build, solve, and lead.

At Halliburton, we invest in our people and empower them to grow their careers while contributing to mission-critical projects across the global energy industry.

Join us and experience the challenges, rewards, and opportunities of working with one of the worlds largest and most advanced energy technology organizations.

As a MidSenior Level Associate Data Scientist, you will play a critical role in developing data-driven insights, supporting digital transformation initiatives, and contributing to advanced analytics and machine learning solutions across the enterprise.

You will work closely with domain experts, engineers, and cross-functional teams to solve complex challenges using data, predictive modeling, and automation.

Key Responsibilities:

Data Engineering & Data Pipeline Development:

- Work closely with engineering and business teams to understand data needs and translate them into scalable data engineering solutions.

- Gather data from diverse structured and unstructured sources, including enterprise data systems, IoT devices, sensors, and operational logs.

- Perform advanced data profiling, data quality assessment, cleansing, transformations, and feature engineering.

- Support the development of automated ETL/ELT data pipelines using modern data engineering tools and cloud technologies.

- Ensure proper documentation, data governance, lineage tracking, and adherence to data security standards.

Data Analysis & Exploration:

- Conduct exploratory data analysis (EDA) to identify patterns, trends, anomalies, and actionable insights.

- Leverage statistical techniques to derive descriptive and diagnostic analytics for engineering and business stakeholders.

- Collaborate with subject matter experts to validate insights and contextualize findings.

Machine Learning Model Development:

- Under general supervision, design, develop, and evaluate machine learning models for prediction, classification, recommendation, and optimization use cases.

- Apply algorithms including regression, clustering, random forests, gradient boosting, ensemble models, and neural networks.

- Implement robust model validation, cross-validation, performance tuning, and error analysis.

- Use MLOps best practices to version models, track experiments, and maintain reproducibility.

Model Deployment & Operationalization:

- Work with engineering teams to deploy machine learning models into production using APIs, cloud platforms, or container-based architectures.

- Help set up monitoring processes to track model performance, data drifts, and system health.

- Contribute to automation of retraining pipelines and lifecycle management of deployed models.

Dashboarding, Visualization & Reporting:

- Build intuitive dashboards and visualization layers using tools like Power BI, Tableau, or cloud-native visualization tools.

- Present insights, analytical outcomes, and recommendations to cross-functional teams and senior stakeholders.

- Translate complex quantitative findings into clear business narratives and impact-driven reports.

Collaboration, Documentation & Continuous Learning:

- Work in agile, cross-functional teams alongside data engineers, software developers, domain experts, and operations teams.

- Create detailed documentation for data processes, algorithms, and models.

- Stay updated on emerging ML/AI technologies, cloud analytics platforms, and open-source frameworks.

- Contribute to internal knowledge-sharing, PoCs, and innovation initiatives.

Required Qualifications:

- Bachelors/Masters degree in STEM (Science, Technology, Engineering, Mathematics).

- 3 - 7 years of experience in analytics, data science, or data engineering roles.

- Strong programming skills in Python; familiarity with R is a plus.

- Experience with data manipulation libraries (pandas, NumPy), ML frameworks (scikit-learn, TensorFlow, PyTorch), and visualization tools.

- Understanding of SQL and relational databases; exposure to cloud platforms (AWS, Azure, GCP) preferred.

- Knowledge of version control (Git), API development, and basic MLOps practices is desirable.

Preferred Skills:

- Experience working with large datasets in manufacturing, energy, industrial IoT, or engineering environments.

- Familiarity with big data ecosystems (Spark, Databricks, Hadoop).

- Experience deploying models into production using Docker/Kubernetes.

- Strong analytical thinking, problem-solving ability, and communication skills.

- Ability to work collaboratively across technical and non-technical teams.

Equal Opportunity Statement:

Halliburton is an Equal Opportunity Employer.

All employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation