Data Science Engineer

Research & Development

∙

Remote

∙

May 31, 2024
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Our client is a fast-growing, digitally native commodities trading house, built by a team of industry professionals who aspire to revolutionize the physical commodity trade.

The company is based in Switzerland but operates worldwide and has offices in Latin America, Asia, and Europe. 

We are seeking an exceptional Data Science Engineer with a strong background in working with financial data to join the Development Team. This role requires a deep understanding of financial markets, data engineering, and data science techniques to develop, deploy, and maintain data-driven solutions that drive our business forward. The position will focus on leveraging financial data for portfolio optimization, risk management, and trading strategy enhancements.

Responsibilities

  • Build, train, and deploy data science models using complex financial data to address business challenges and opportunities;
  • Establish and maintain processing methodologies for financial data sources; ensure data quality, accuracy, and relevance in a trading environment;
  • Translate complex financial insights into specific data science projects, aligning strategies with trading objectives and risk management goals;
  • Conduct rigorous evaluation of data science models, focusing on performance metrics and alignment with business KPIs;
  • Implement and manage version control tools like Git for effective tracking and collaborative model development;
  • Create dynamic visualization tools and reporting dashboards to communicate financial insights and model outputs effectively;
  • Extract valuable insights from financial data using advanced data science techniques, statistical analysis, and domain expertise;
  • Collaborate on developing and implementing advanced portfolio optimization strategies utilizing Cvx Portfolio and other financial analytics tools;
  • Assist in designing and evaluating risk management strategies based on sophisticated financial data analysis;
  • Conduct paper trades to test, validate, and fine-tune data science models and strategies using historical financial data.

Requirements

  • Minimum of 5 years of hands-on experience in Data Science with a focus on financial data analysis, modeling, and deployment in trading environments;
  • Strong understanding of financial markets, instruments, trading strategies, and risk management principles. Experience in commodities trading is a plus;
  • Proficient in processing and analyzing large-scale financial datasets. Expertise in Python and financial data processing/analytics libraries (Pandas, NumPy, SciPy);
  • Fluency in relevant AWS services for financial data analysis such as Sagemaker and Athena;
  • Proficient in SQL for querying relational databases housing financial data;
  • Proven experience in developing interpretable data science models specifically tailored for financial use cases;
  • Strong proficiency in standard data science and machine learning libraries for financial analytics;
  • Experience working with time series modeling and analysis in the context of financial data;
  • Ability to translate complex financial data insights into actionable strategies and models;
  • Strong foundation in applying descriptive and predictive statistics in financial data analysis;
  • Familiarity with version control tools like Git/Github in a financial data science context;
  • Demonstrated experience with portfolio optimization techniques and tools, particularly in a financial domain;
  • Sound knowledge and experience in designing, evaluating, and implementing hedging strategies and risk management techniques in finance;
  • Excellent English communication skills, both written and verbal, to effectively convey complex financial concepts and insights.

Bonus points for

We offer excellent benefits, including but not limited to

  • People-oriented management without bureaucracy;
  • Competitive compensation;
  • 25 working days of annual paid vacation;
  • Paid sick-leaves;
  • Relocation support;
  • Friendly and engaging professional team;
  • Opportunities for self-realization, career, and professional growth.

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