Senior Quantitative Researcher
hace 1 semana
About usRavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. RavenPack's products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. Our clients include the most successful hedge funds, banks, and asset managers in the world
Join RavenPack:RavenPack is searching for a Senior Quantitative Researcher to join the Data Science - QIS Team at our Spanish headquarters.
As a Senior Quant Researcher, you will be participating in the development of new systematic trading strategies, as well as showcasing the value of our data for trading and investment purposes across equities mostly and time horizons.
The ability to communicate effectively in English both in writing and verbally is a must.
European legal working status is required.
We offer competitive compensation, active mentoring, exposure to the top trading firms, and a fun working environment. Relocation assistance is available.
Your Responsibilities:As a Senior Quant Researcher, you will join the "Quantitative Investment Strategies (QIS) Team," consisting of four quant researchers dedicated to feature engineering and developing systematic trading strategies. Your work will primarily involve bottom-up research, with some top-down research opportunities. You will create white papers to enhance RavenPack's reputation as a thought leader in the alternative data industry and present trading strategies to quantitative analysts. You will independently work on practical use cases that demonstrate the value of RavenPack data. Additionally, your responsibilities will include:
Identifying, validating, and amplifying predictive signals within our data while discerning and filtering out irrelevant information.
Formulating systematic trading strategies spanning multiple asset classes with a major focus on equities, enriching security-selection capabilities with Alternative Data across different holding periods.
Offering data-driven insights, engaging in discussions about your research, and presenting trading strategies to leading quantitative researchers and portfolio managers in the field.
Effectively communicating intricate analytical concepts to management in a clear and concise manner.
What We're Looking For: A PhD/MSc in Quantitative or Computational Finance, or from any related fields including Machine Learning, Econometrics, Applied Mathematics, etc.
5+ years of relevant work experience as a quantitative researcher, manipulating large and noisy alternative datasets for features engineering, signal amplification, and portfolio backtesting.
Outstanding quantitative, analytical, and problem-solving skills, with proven ability to develop original research and hypothesis testing.
Demonstrated proficiency in at least Python and SQL.
Strong enthusiasm for technology, and familiarity with big data technologies coupled with proficiency in machine learning is highly advantageous.
What's in it for you? You will work with the latest technologies.
Our Headquarters is located in Marbella. Following the onboarding period, we offer a hybrid work model, allowing for up to 2 days remote per week.
Free Company shuttle bus from Malaga, Fuengirola, Riviera and Estepona.
You will have ownership of projects working in a collaborative environment where we will value your contribution.
You will work in an agile environment able to react quickly to changes with a fairly flat hierarchy.
As we encourage continuous learning, we will support your ongoing training.
Diversity is in our DNA You will work in an international environment (over 29 nationalities and 24 languages spoken)
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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