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Quantitative Analyst - Time-Series, Econometrics - FinTech

Quantitative Analyst – Forecasting, Time-series, Statistics, Econometrics, Python, R, Supply and Demand, Commodities, Oil, Gas, Spark, Hadoop

FinTech Analytics Platform. London.

Highly Competitive + Benefits + Bonus

A revolutionary FinTech firm has launched a game changing analytics platform aimed at the energy and commodities trading market. They are looking to hire an experienced Quantitative Analysis with a strong background in econometrics and time-series analysis that can help them continue to develop the platform.

As the Quantitative Analyst (econometrics, forecasting, time-series) you will have been working in a role that involves forecasting future prices in a particular asset class. Ideally you will have multi-asset experience and will have used a variety of different models to predict future prices. You will have be very comfortable working with time-series analysis and using statistical/probability mathematical models. Your role will be to expand the time horizons that predictions are made within the commodities and energy markets, with a focus on oil flow. The platform uses Spark and Hadoop to manage their Big Data and you will be using that data in your prediction models.

The firm have been growing over the last 18 months and it’s a great change to join somewhere that you can make a real difference to. You will have the chance to shape the platform going forward as well as benefitting in the firms growth.

The Quantitative Analyst (econometrics, forecasting, time-series) will need:

  • Strong experience with econometrics and time-series modelling – Essential
  • Experience in more than one asset class (e.g. Fixed Income, Credit, Equities, Commodities etc) – Essential
  • Experience with R or Python – Essential
  • Experience with supply and demand contracts – Beneficial
  • Experience with Spark or Hadoop – Beneficial

Please get in touch with Tom Kemp at Harrington Starr for more information.

Quantitative Analyst – Forecasting, Time-series, Statistics, Econometrics, Python, R, Supply and Demand, Commodities, Oil, Gas, Spark, Hadoop