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.

REQUIREMENTS:

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

KEY SKILLS:

• Deep knowledge of machine learning, statistical modelling, and Geo statistics.
• Strong programming and data engineering skills.
• Understanding of the oil and gas lifecycle, from exploration to production.
• Excellent problem-solving and communication abilities.
• Proven track record of innovation in oil and gas analyses
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technical expertise, and crafting data-driven solutions to meet business requirements.

• Define and implement data strategies to support exploration, drilling, and production business goals.
• Oversee data collection, cleaning, and integration from diverse sources (e.g., seismic, production logs, IoT sensors, SCADA systems).
• Ensure data accuracy, consistency, and security while adhering to industry compliance standards.
• Design and implement advanced machine learning models (e.g., predictive maintenance, reservoir simulations, production optimization).
• Develop algorithms for seismic data interpretation, reservoir characterization, and well-performance forecasting.
• Optimize workflows using natural language processing (NLP) for unstructured data, such as drilling reports and maintenance logs.
• Conduct exploratory data analysis (EDA) to identify trends, anomalies, and optimization opportunities.
• Utilize geospatial analysis and geostatistical techniques to interpret geological and geophysical data.
• Implement real-time data analytics for drilling, well monitoring, and production enhancement.
• Lead the adoption of cloud-based data platforms (e.g., Azure, AWS, Google Cloud) for scalable computation.
• Stay updated on emerging technologies for oil and gas applications, such as edge computing, digital twins, and advanced AI.
• Drive automation of repetitive tasks using advanced scripting and machine learning pipelines.
• Mentor junior data scientists and engineers, fostering a culture of innovation and excellence.
• Collaborate with engineers, geophysicists, and reservoir managers to translate business challenges into data science solutions.
• Communicate technical findings to non-technical stakeholders through visualizations and reports.
• Develop optimization models for energy efficiency, cost reduction, and supply chain logistics.
• Enhance drilling accuracy and reduce downtime through predictive analytics for equipment maintenance.
• Implement risk assessment models to improve safety and compliance standards.
• Expertise in Python, R, MATLAB, and SQL for statistical modelling and data analysis.
• Proficient in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and big data tools (e.g., Hadoop, Spark).
• Hands-on experience with visualization tools like Power BI, Tableau, or D3.js.
• Familiarity with domain-specific software like Petrel, Schlumberger, or Halliburton’s Decision Space

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

• Lead the design and implementation of end-to-end data science projects, from defining

business problems to deploying advanced machine learning models at scale.

• Architect and build predictive models, statistical algorithms, and machine learning systems to

solve complex business challenges and enhance decision-making.

• Provide technical leadership and mentorship to junior data scientists, promoting best practices

and guiding technical development across the team.

• Innovate new methodologies in machine learning and AI, staying at the forefront of emerging

tools, techniques, and industry trends.

• Lead cross-functional teams, ensuring alignment and seamless integration between data

science, engineering, and business teams.

• Implement robust data governance, validation, and quality assurance processes to ensure the

integrity and reliability of data science outputs.

• Partner with data engineering and IT teams to build scalable, automated data pipelines that

support data science initiatives.

• Present complex data insights and machine learning results to non-technical stakeholders,

ensuring clarity and business relevance.

• Drive the design and execution of experiments, A/B tests, and statistical analyses to measure

and optimize the impact of data-driven decisions.

• Ensure compliance with regulatory requirements, data security, and governance protocols

when building scalable data science solutions.

• Understand client needs and provide tailored, strategic solutions that align with business

objectives.

• Build strong relationships by clearly communicating technical concepts and managing

expectations.

• Actively participate in recruiting top technical talent for the team.
• Collaborate with the sales team in presales activities, identifying client needs, providing

technical expertise, and crafting data-driven solutions to meet business requirements.

• Define and implement data strategies to support exploration, drilling, and production business goals.
• Oversee data collection, cleaning, and integration from diverse sources (e.g., seismic, production logs, IoT sensors, SCADA systems).
• Ensure data accuracy, consistency, and security while adhering to industry compliance standards.
• Design and implement advanced machine learning models (e.g., predictive maintenance, reservoir simulations, production optimization).
• Develop algorithms for seismic data interpretation, reservoir characterization, and well-performance forecasting.
• Optimize workflows using natural language processing (NLP) for unstructured data, such as drilling reports and maintenance logs.
• Conduct exploratory data analysis (EDA) to identify trends, anomalies, and optimization opportunities.
• Utilize geospatial analysis and geostatistical techniques to interpret geological and geophysical data.
• Implement real-time data analytics for drilling, well monitoring, and production enhancement.
• Lead the adoption of cloud-based data platforms (e.g., Azure, AWS, Google Cloud) for scalable computation.
• Stay updated on emerging technologies for oil and gas applications, such as edge computing, digital twins, and advanced AI.
• Drive automation of repetitive tasks using advanced scripting and machine learning pipelines.
• Mentor junior data scientists and engineers, fostering a culture of innovation and excellence.
• Collaborate with engineers, geophysicists, and reservoir managers to translate business challenges into data science solutions.
• Communicate technical findings to non-technical stakeholders through visualizations and reports.
• Develop optimization models for energy efficiency, cost reduction, and supply chain logistics.
• Enhance drilling accuracy and reduce downtime through predictive analytics for equipment maintenance.
• Implement risk assessment models to improve safety and compliance standards.
• Expertise in Python, R, MATLAB, and SQL for statistical modelling and data analysis.
• Proficient in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and big data tools (e.g., Hadoop, Spark).
• Hands-on experience with visualization tools like Power BI, Tableau, or D3.js.
• Familiarity with domain-specific software like Petrel, Schlumberger, or Halliburton’s Decision Space.