Lead Data Scientist

Company Overview

10Pearls is an end-to-end digital technology services partner helping businesses utilize technology as a competitive advantage. We help our customers digitalize their existing business, build innovative new products, and augment their existing teams with high-performance team members. Our broad expertise in product management, user experience/design, cloud architecture, software development, data insights and intelligence, cybersecurity, emerging tech, and quality assurance ensures that we are delivering solutions that address business needs. 10Pearls is proud to have a diverse clientele including large enterprises, SMBs, and high-growth startups. We work with clients across industries, including healthcare/life sciences, education, energy, communications/media, financial services, and hi-tech. Our many long-term, successful partnerships are built upon trust, integrity, and successful delivery and execution.

Role

As a Principal/Lead Data Scientist you will spearhead advanced analytics initiatives, leveraging data-driven insights to optimize exploration, production, and operational efficiency. Your role involves building predictive models, deploying machine learning algorithms, and leading a team to solve complex challenges unique to the industry.

Responsibilities

1. Lead the design and implementation of end-to-end data science projects, from defining business problems to deploying advanced machine learning models at scale.
2. Architect and build predictive models, statistical algorithms, and machine learning systems to solve complex business challenges and enhance decision-making.
3. Provide technical leadership and mentorship to junior data scientists, promoting best practices and guiding technical development across the team.
4. Innovate new methodologies in machine learning and AI, staying at the forefront of emerging tools, techniques, and industry trends.
5. Lead cross-functional teams, ensuring alignment and seamless integration between data science, engineering, and business teams.
6. Implement robust data governance, validation, and quality assurance processes to ensure the integrity and reliability of data science outputs.
7. Partner with data engineering and IT teams to build scalable, automated data pipelines that support data science initiatives.
8. Present complex data insights and machine learning results to non-technical stakeholders, ensuring clarity and business relevance.
9. Drive the design and execution of experiments, A/B tests, and statistical analyses to measure and optimize the impact of data-driven decisions.
10. Ensure compliance with regulatory requirements, data security, and governance protocols when building scalable data science solutions.
11. Understand client needs and provide tailored, strategic solutions that align with business objectives.
12. Build strong relationships by clearly communicating technical concepts and managing expectations.
13. Actively participate in recruiting top technical talent for the team.
14. Collaborate with the sales team in presales activities, identifying client needs, providing technical expertise, and crafting data-driven solutions to meet business requirements.
15. Define and implement data strategies to support exploration, drilling, and production business goals.
16. Oversee data collection, cleaning, and integration from diverse sources (e.g., seismic, production logs, IoT sensors, SCADA systems).
17. Ensure data accuracy, consistency, and security while adhering to industry compliance standards.
18. Design and implement advanced machine learning models (e.g., predictive maintenance, reservoir simulations, production optimization).
19. Develop algorithms for seismic data interpretation, reservoir characterization, and well-performance forecasting.
20. Optimize workflows using natural language processing (NLP) for unstructured data, such as drilling reports and maintenance logs.
21. Conduct exploratory data analysis (EDA) to identify trends, anomalies, and optimization opportunities.
22. Utilize geospatial analysis and geostatistical techniques to interpret geological and geophysical data.
23. Implement real-time data analytics for drilling, well monitoring, and production enhancement.
24. Lead the adoption of cloud-based data platforms (e.g., Azure, AWS, Google Cloud) for scalable computation.
25. Stay updated on emerging technologies for oil and gas applications, such as edge computing, digital twins, and advanced AI.
26. Drive automation of repetitive tasks using advanced scripting and machine learning pipelines.
27. Mentor junior data scientists and engineers, fostering a culture of innovation and excellence.
28. Collaborate with engineers, geophysicists, and reservoir managers to translate business challenges into data science solutions.
29. Communicate technical findings to non-technical stakeholders through visualizations and reports.
30. Develop optimization models for energy efficiency, cost reduction, and supply chain logistics.
31. Enhance drilling accuracy and reduce downtime through predictive analytics for equipment maintenance.
32. Implement risk assessment models to improve safety and compliance standards.
33. Expertise in Python, R, MATLAB, and SQL for statistical modelling and data analysis.
34. Proficient in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and big data tools (e.g., Hadoop, Spark).
35. Hands-on experience with visualization tools like Power BI, Tableau, or D3.js.
36. Familiarity with domain-specific software like Petrel, Schlumberger, or Halliburton’s Decision Space.

Requirements:

1. Advanced degree (master's or PhD) in Data Science, Petroleum Engineering, Geophysics, Computer Science, or a related field.
2. 7+ years of experience in data science, with at least 3 years in oil and gas.
3. Strong understanding of petroleum systems, reservoir engineering, and upstream/downstream operations.
4. Demonstrated success in leading data-driven projects within the oil and gas sector.

Key Skills:

1. Deep knowledge of machine learning, statistical modelling, and Geo statistics.
2. Strong programming and data engineering skills.
3. Understanding of the oil and gas lifecycle, from exploration to production.
4. Excellent problem-solving and communication abilities.
5. Proven track record of innovation in oil and gas analyses.