Quantitative Research Specialist
We are looking for a Quantitative Research Specialist for one of our clients – an American systematic hedge fund, leveraging curation-edge technologies and machine learning on data to deliver exceptional returns to their investors.
Manpower is part of ManpowerGroup, a Fortune 500 Global Business. We’ve been powered by our people since 1948. That’s 70 years of making dreams a reality. We don’t make our people; we are made by them and have been since the very beginning. We create powerful connections between organizations and the talent they need to unleash workforce potential
Responsibilities:
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Conduct in-depth research, design, and deploy advanced predictive machine learning models, actively engaging in hands-on development.
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Formulate and implement systematic trading strategies based on model predictions, contributing to effective decision-making.
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Stay abreast of financial literature and academic papers to identify market inefficiencies and draw inspiration for enhancing trading strategies.
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Develop hypotheses related to market patterns and dependencies, conducting rigorous testing to validate findings.
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Acquire expertise in financial markets and instruments, exploring innovative ways to predict future performance.
Technical Requirements:
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Possess at least a graduate degree in a technical field, demonstrating an eagerness to learn about financial markets and predictive analytics.
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Accumulate 5+ years of experience analyzing substantial datasets using Python or R.
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Exhibit proficiency in training and implementing models with Scikit-learn, PyTorch, and/or TensorFlow.
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Showcase understanding in linear algebra, time series analysis, data mining, numerical methods, and statistical tools, including PCA and regression.
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Demonstrate a track record of dedication and perseverance in solving complex problems.
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Exhibit excellent spoken and written English skills.
Soft Skills Requirements:
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Demonstrate exceptional communication skills, both written and oral.
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Possess self-awareness, intellectual curiosity, and a collaborative team-player mentality.
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Exhibit a self-starting, enthusiastic, and positive thinking approach.
Nice to Have:
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Degrees in technical or quantitative disciplines such as statistics, mathematics, physics, electrical engineering, or computer science.
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Exposure to packages like pandas, NumPy, statsmodels, scikit-learn, scipy, matplotlib, and TensorFlow. C++ experience is advantageous.
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Familiarity with the Agile approach and methodology.
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Experience or interest in machine learning techniques, time series analysis, and econometrics.
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Proficiency in Deep Learning: DNN, CNN, RNN/LSTM, GAN, or other autoencoders.
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Familiarity with KX/KDB+ or q experience.
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Basic understanding of equity markets.
Company Culture:
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Thrive in a meritocratic, ego-free, and highly driven team environment.
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Enjoy perpetual personal and academic development with access to a top-notch Wall St and technology research library.
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Tackle challenging and innovative problems daily, with opportunities to explore personal research projects that contribute meaningfully to the business.
Benefits & Work Conditions:
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Standard work hours, 8 am – 6 pm Sofia time, with 100% in-office presence and off days on select US holidays.
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Generous 28 days of paid time off, including 6 sick days.
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Access to unlimited free SoftUni courses for continued education and learning.
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Convenient parking and garage facilities.
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Unlimited high-quality espresso and coffee in the office.
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Provided office lunches on Mondays and Fridays.
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Healthy snacks and beverages available in the office.
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Free fitness/gym subscription.
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Subsidy towards additional health insurance.
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Engage in team-building events.
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Modern office with a pleasant environment, high-tech equipment, and stunning city and mountain views from the top floor.
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Participate in the referral program for bringing in new talent.
Modern office with a pleasant environment, high-tech equipment, and stunning city and mountain views from the top floor.
Participate in the referral program for bringing in new talent.
Develop hypotheses related to market patterns and dependencies, conducting rigorous testing to validate findings.
Acquire expertise in financial markets and instruments, exploring innovative ways to predict future performance.