What are the responsibilities and job description for the Experienced Quant Portfolio Manager or Strategies position at SMB Capital?
SMB Capital and Kershner Trading Group, a joint venture of leading proprietary trading
and technology firms with offices in New York, Austin, and Chicago, are seeking
Experienced Quantitative Portfolio Managers / Strategists for the U.S. equity market and
Crypto currency.
SMB Capital/Kershner Trading Group is a collaborative research environment and is
seeking individuals with a strong entrepreneurial spirit, exceptional work ethic, and
strong analytical skills to develop new trading strategies. The firm provides a cutting
edge data platform, high performance elastic research and trading infrastructure,
investment capital and trader coaching/support. We provide access to rich datasets
(e.g., tick data, fundamental datasets, sentiment and other alternative datasets), a state-
of-the-art research environment ideal for machine learning, integrated simulation and
production environments with co-located execution engines and advanced risk
management and monitoring tools.
Ideal candidates will have an MS or PhD in an Engineering or Pure Science discipline
with expertise in alpha research, portfolio construction, risk management and trade
execution. Relevant quantitative skillsets include Artificial Intelligence, Machine
Learning, Natural Language Processing, Portfolio Optimization, Linear Programming,
Time Series Prediction, Factor Analysis and/or Fundamental Equity
Valuation. Candidates should have a proficiency in one of the following programming
languages: Python (preferred) and/or C , C#, Java or R. Candidate should have
recent track record or demonstrate a direct contribution to profitable systematic trading
strategies or process in U.S. Equities and cryptos. Intraday strategies and medium to
high frequency are preferred. Experience with futures, FX and international equity
trading is also a plus. Candidates should have the ability to deploy and manage trading
strategies from inception.
Opportunities are available in the New York office with some options available for
remote teams and team members.
and technology firms with offices in New York, Austin, and Chicago, are seeking
Experienced Quantitative Portfolio Managers / Strategists for the U.S. equity market and
Crypto currency.
SMB Capital/Kershner Trading Group is a collaborative research environment and is
seeking individuals with a strong entrepreneurial spirit, exceptional work ethic, and
strong analytical skills to develop new trading strategies. The firm provides a cutting
edge data platform, high performance elastic research and trading infrastructure,
investment capital and trader coaching/support. We provide access to rich datasets
(e.g., tick data, fundamental datasets, sentiment and other alternative datasets), a state-
of-the-art research environment ideal for machine learning, integrated simulation and
production environments with co-located execution engines and advanced risk
management and monitoring tools.
Ideal candidates will have an MS or PhD in an Engineering or Pure Science discipline
with expertise in alpha research, portfolio construction, risk management and trade
execution. Relevant quantitative skillsets include Artificial Intelligence, Machine
Learning, Natural Language Processing, Portfolio Optimization, Linear Programming,
Time Series Prediction, Factor Analysis and/or Fundamental Equity
Valuation. Candidates should have a proficiency in one of the following programming
languages: Python (preferred) and/or C , C#, Java or R. Candidate should have
recent track record or demonstrate a direct contribution to profitable systematic trading
strategies or process in U.S. Equities and cryptos. Intraday strategies and medium to
high frequency are preferred. Experience with futures, FX and international equity
trading is also a plus. Candidates should have the ability to deploy and manage trading
strategies from inception.
Opportunities are available in the New York office with some options available for
remote teams and team members.
