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We are interested in researching and taking in live a variety of quantitative strategies based on historic and live market data, alternative datasets, social media data (both audio and video) and stock fundamental data.
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- We are looking for an experienced data engineer to join our team.
- The preprocessing involves ETL tasks, using pyspark, AWS Glue, staging data in parquet formats on S3, and Athena
To succeed in this data engineering position, you should care about well-documented, testable code and data integrity. We have devops who can help with AWS permissions.
We would like to build up a consistent data lake with staged, ready-to-use data, and to build up various scripts that will serve as blueprints for various additional data ingestion and transforms.
If you enjoy setting up something which many others will rely on, and have the relevant ETL expertise, we’d like to work with you.
Responsibilities
- Analyze and organize raw data
- Build data pipelines
- Prepare data for predictive modeling
- Explore ways to enhance data quality and reliability
- Potentially, collaborate with data scientists to support various experiments
Requirements
- Previous experience as a data engineer with the above technologies
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Data ScienceWe are a nascent quantitative hedge fund led by an MIT PhD and Math Olympiad medallist, offering opportunities to grow with us as we build out the team. Our fund has world class investors and big data experts as part of the GP, top-notch ML experts as advisers to the fund, plus has equity funding to grow the team, license data and scale the data processing.
We are interested in researching and taking in live a variety of quantitative strategies based on historic and live market data, alternative datasets, social media data (both audio and video) and stock fundamental data.
You would join, and, if qualified, lead a growing team of data scientists and researchers, and be responsible for a complete lifecycle of quantitative strategy implementation and trading.
Requirements:
- Atleast 3 years of relevant ML experience
- Graduation date : 2018 and earlier
- 3-5 years of experience in high level Python programming.
- Master Degree (or Phd) in quantitative disciplines such as Statistics, Mathematics, Physics, Computer Science in top universities.
- Good knowledge of applied and theoretical statistics, linear algebra and machine learning techniques.
- Ability to leverage financial and statistical insights to research, explore and harness a large collection of quantitative strategies and financial datasets in order to build strong predictive models.
- Should take ownership for the research, design, development and implementation of the strategy development and effectively communicate with other team mates
- Prior experience and good knowledge of lifecycle and pitfalls of algorithmic strategy development and modelling.
- Good practical knowledge in understanding financial statements, value investing, portfolio and risk management techniques.
- A proven ability to lead and drive innovation to solve challenges and road blocks in project completion.
- A valid Github profile with some activity in it
Bonus to have:
- Experience in storing and retrieving data from large and complex time series databases
- Very good practical knowledge on time-series modelling and forecasting (ARIMA, ARCH and Stochastic modelling)
- Prior experience in optimizing and back testing quantitative strategies, doing return and risk attribution, feature/factor evaluation.
- Knowledge of AWS/Cloud ecosystem is an added plus (EC2s, Lambda, EKS, Sagemaker etc.)
- Knowledge of REST APIs and data extracting and cleaning techniques
- Good to have experience in Pyspark or any other big data programming/parallel computing
- Familiarity with derivatives, knowledge in multiple asset classes along with Equities.
- Any progress towards CFA or FRM is a bonus
- Average tenure of atleast 1.5 years in a company
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