Machine Learning Engineer at Kensho
🇺🇸 United States › Massachusetts › Cambridge (Posted Jul 11 2018)
About the company
Based in Harvard Square and New York City, Kensho deploys scalable machine intelligence and analytics systems across the most critical government and commercial institutions in the world to solve some of the hardest analytical problems of our time. In 2016 Kensho was named by Fortune as one of the "5 Hottest Companies in Fintech," and it topped Forbes' inaugural and 2016 lists of the most innovative Fintech companies in the world. In 2017 at Davos, Kensho was named a "Technology Pioneer" by the World Economic Forum—one of most innovative technology companies—of any kind—in the world. In 2018, Kensho was acquired by S&P Global (SPGI) in the largest acquisition of an artificial intelligence company to-date. Kensho will operate as an independent entity within SPGI.
We are looking for a talented and creative individual to join our team of Machine Learning Engineers. As a ML Engineer at Kensho, you will tackle a wide range of problems from timeseries prediction to natural language processing and are passionate about building machine learning systems on real world data. Do you have extensive experience applying a range of ML models to a diverse set of problems? Do you enjoy moving beyond the theoretic confines of academia to apply your tradecraft in the real world? Does producing data-driven products that will empower decision makers at all levels of the global banking industry and beyond excite you? If so, we want to hear from you.
We take pride in our team-based, tightly-knit startup community that provides our employees with an environment to bring transparency to the biggest challenges in data.
What You’ll Do:
Conduct original research on large proprietary and open source data sets
Identify, research, prototype, and build predictive products
Build cutting-edge models for understanding vast amounts of textual data
Write production-ready code
Write tests to ensure the robustness and reliability of your productionized models
Skills & requirements
What We Look For:
At least one core programming expertise, such as python (NumPy, SciPy, Pandas), MATLAB, or R
Experience with advanced machine learning methods
Strong statistical knowledge, intuition, and experience applying machine learning models to real data
Stellar ability to communicate even the most complicated methods and results to a broad, often non-technical audience
Effective coding, documentation, and communication habits
Ability and credibility to direct a team
Several of the following terms should hold deep meaning for you: lookahead bias, bagging, boosting, stacking, information retrieval, entity recognition, bootstrapping, LSTM, Glorot initialization, Kullback-Leibler divergence, GLOVE, SMAPE, HMM, MAP, exponential family, VC dimension, EM, L1, TD(Lambda)
How to Really Get Our Attention:
3+ years of experience being a major machine learning contributor at a top company, hedge fund, or university
Your github/kaggle profile showing a project or problems you’ve tackled
Technologies We Like:
Python and specifically Numpy, SciPy, Pandas, scikit-learn
Neural network packages like TensorFlow and Torch
ML packages like LightGBM and XGBoost
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