Senior Data Scientist

Houston, Texas
Nov 05, 2023
Nov 22, 2023
Engineering, Software
Position Type
Scientist, Data
Full Time
Organization Type
Corporate, Other Corporate
Title: Senior Data Scientist

Mercuria is a major player in the Physical and Financial Global Commodity markets, with major trading centers in London, Geneva, Houston, Singapore, Shanghai and Beijing. We actively trade in all the major commodity asset classes, from crude and refined oil products, to power & gas, LNG, coal and emissions, through to freight, metals, and agricultural products.

We operate a global diversified technology team across key hubs such as Geneva, London, Houston and Singapore, while operating in close partnership with some strategic co-development centers in Bucharest, Bangalore and Hyderabad. All teams run an Agile delivery model in unison with our business partners to deliver multi-asset-class commodity systems, with a focus on automation, user experience, optimization, innovation and control.

The role

This is a great opportunity to join one of the largest integrated energy and commodity trading companies in the world. We are looking for a hands-on Senior Data Scientist with experience in the energy markets of North America. As a Senior Data Scientist you will be responsible for leading complex data analysis projects, developing advanced machine learning models, and providing strategic insights that contribute to our business growth and innovation.

Key responsibilities
  • Lead end-to-end data science projects, from problem formulation and data collection to model deployment and result presentation.
  • Collaborate with cross-functional teams to identify business challenges and opportunities that can be addressed using data analysis and machine learning techniques.
  • Design and implement innovative machine learning algorithms and models to solve complex business problems, such as predictive modeling, recommendation systems, and anomaly detection.
  • Deliver technical solutions to evolve our trading platforms and solve business problems. This involves requirements gathering, identifying priorities, planning tasks and on-time delivery of solutions.
  • Explore, clean, and preprocess large datasets to extract relevant features and ensure data quality for analysis.
  • Apply statistical analysis methods to uncover patterns, trends, and insights from data, and effectively communicate findings to both technical and non-technical stakeholders.
  • Mentor and provide guidance to junior data scientists, helping them grow their technical and analytical skills.
  • Stay up-to-date with the latest advancements in data science and machine learning techniques, and apply them to enhance our analytics capabilities.
  • Collaborate with engineering teams to integrate data-driven solutions into production systems and ensure scalability and reliability.
  • Participate in data-driven decision-making discussions with senior management and contribute to the overall data strategy of the company.

Skill and Experience
  • BS/MS in Engineering/Statistics/Computer Science/Economics or a related field
  • Experience working with energy and financial data, such as energy prices, supply and demand data, and economic indicators.
  • Knowledge of energy commodities trading, especially with North American Gas and Power Markets.
  • 7-10+ years' experience developing enterprise level models and applications with cloud-based micro services such as AWS Lambda, S3, ECR, Sagemaker, etc.
  • Strong SQL proficiency and understanding of database technologies, such as AWS RDS and Snowflake
  • Strong proficiency in Python, with knowledge of common ML and statistical packages such as Scikit-Learn, SciPy, Prophet, etc
  • Advanced knowledge of Generalized Linear and Non-Linear Models, Time Series Analysis, Random Forest, Gradient Boosted Machines, Neural Networks
  • Proficiency in data visualization tools (e.g., Tableau) to effectively communicate insights.
  • Solid understanding of statistical concepts and hypothesis testing. Experience working with large-scale datasets, databases, and data processing tools (e.g., SQL, Spark).
  • A portfolio showcasing previous data science projects and their business impact is highly desirable.s