Data Scientist and Machine Learning Engineer

Satoshi Systems is a company founded by experts in all aspects of Sourcing, Trading, Financing and Risk Management of commodities such as Agris, Metals and Energy. We believe that by taking the lead in providing innovative solutions based on disruptive technologies such as Blockchain, IoTs, AR/VR and Machine Learning we can bring about significant changes in the existing cost structure of the entire commodity supply chain and financing ecosystem. Companies which will adopt Satoshi solutions will not just realize decreased cost, increased efficiency and real-time risk management capabilities but will also gain tremendous competitive advantage.

Our flagship product is a blockchain based commodity inventory and trade financing SaaS for which we are looking for a Data Scientist and Machine Learning Engineer. We seek an ambitious, independent data science professional who can understand real-world business problems & quickly translate them into high quality, reusable machine learning models & code. This person has probably spent most of their life coding, entered Kaggle competitions, has built production quality ML systems & loves the idea of applying cutting-edge Machine Learning to incredibly challenging forecasting problems. You believe that it’s possible to use AI, Machine Learning & interesting data sources to help people make better decisions in the world of Commodities, Finance and Trading.

We are a flat and open organization, a challenging but fun-loving culture, learning programs to develop deep expertise in the commodity industry and overall the opportunity to influence your team and create a software system you can be proud of.

Responsibilities of the Data Scientist and Machine Learning Engineer

  • Conduct independent research to take a business problem, such as forecasting crop yields using data from several sources such as Satellite & Drone Images, Farm Sensors, Historical data etc, from specification to a fully validated & coded model.
  • Run rigorous experiments to evaluate & explain new features, datasets & models. We like people who can bring their own opinions, experience & research to the table.
  • Work with engineers to put research models & pipelines into a production-ready SaaS platform.
  • Collaborate with our application developers early in the SaaS development stage to ensure that the application does not just satisfy the transactional requirements but also the complex analytical requirements
  • Collaborate with other data scientists and engineers to enhance and develop our in-house data science libraries & pipelines. These APIs & tools are essential for the future of the business & you’ll have tremendous input into the way they are designed at this early stage.
  • You will not hesitate from rolling your sleeves and jump head-on into the pools of raw dirty data provided by our customers. In-fact you would apply your machine learning skills such that our system can learn from experience and clean the data itself

Skills

  • A strong, quantitative degree/PhD (Maths, Physics, AI/Machine Learning, Statistics etc.)
  • Passion for exploration, experimentation, data analysis & machine learning
  • Extremely strong coding skills in at least one language, preferably Python.
  • Knowledge of the major data science tools and frameworks (python, pandas, sklearn, tensorflow, R, spark, SQL etc.)

Bonus points for

  • Experience or strong interest relating to trading, financial or energy markets
  • Experience applying AI or Machine Learning to multivariate time series or sequential prediction problems (adjusting traditional ML, state space models & gaussian process regression, LSTMs etc.)

Required Certifications/Qualifications

A PhD in statistical or machine learning or a related field will be highly regarded For the really talented candidate, the qualification requirement can be relaxed

Location and Travel

  • Permanent. London.
  • Starting Immediately
  • 20% travel

Compensation & Benefits

  • £ 90K
  • Equity participation in the company

Apply Now!