With the continued impact of COVID-19 and social distancing measures, Kensho fully supports remote interviewing and onboarding procedures.
At Kensho, we hire talented people and give them the autonomy, support, and resources needed to build cutting edge technology and products for our parent company, S&P Global. As a result, we produce technology that is scalable, robust, and solves the challenges of one of the world’s largest, most successful financial institutions.
As a Machine Learning Engineer, you will be responsible for tackling a wide range of problems from natural language processing to timeseries prediction. You will move beyond the theoretical confines of academia and apply your tradecraft to build machine learning systems on real world data. In this role, you will constantly collaborate and work with groups of engineers. Our Machine Learning team is responsible for delivering ML-based solutions from problem framing, to prototyping, and production application.
Are you looking to make impactful, scalable contributions that transform the way we think about data? If so, we would love to help you excel here at Kensho. We take pride in our tightly-knit, team-based community that provides our Kenshins with a collaborative, supportive environment to bring transparency to the biggest challenges in data.
What You’ll Do
- You will conduct original research on large proprietary and open source data sets
- Identify, research, prototype, and build predictive products
- You will build cutting-edge models for understanding vast amounts of textual data
- Write production code
- You will write tests to ensure the robustness and reliability of your productionized models
- You will work closely with software engineers to build incredible systems
Technologies You’ll Use
- Python and specifically NumPy, SciPy, Pandas, SciKit-learn
- Neural network packages like TensorFlow/Keras and pytorch
- ML packages like LightGBM and XGBoost
- OpenFST
- Elasticsearch
Required Skills, Knowledge & Experience
- You have 5+ years of experience being a major machine learning contributor at a top company, hedge fund, or university
- You have at least one core programming expertise, such as Python (NumPy, SciPy, Pandas), MATLAB, or R
- You have experience with advanced machine learning methods
- You possess strong statistical knowledge, intuition, and experience modeling real data
- You demonstrate effective coding, documentation, and communication habits
- You have a strong research acumen and experiment design skills
- You have the ability and credibility to lead a team
- You have a stellar ability to communicate even the most complicated methods and results to a broad, often non-technical audience
- Several of the following terms should hold deep meaning for you: LSTM, lookahead bias, bagging, boosting, stacking, information retrieval, batch norm, entity recognition, bootstrapping, Glorot initialization, Kullback-Leibler divergence, GLOVE, SMAPE, HMM, MAP, exponential family, VC dimension, EM, L1, TD(Lambda)
How to Really Get Our Attention
- Your GitHub/Kaggle profile shows a project or problems you’ve tackled
- You have relevant research or publications
- You are a specialist in deep learning in an academic setting or production-grade machine learning
Benefits & Perks
At Kensho, we pride ourselves on providing top-of-market benefits, including:
- Medical, Dental, and Vision insurance
- 100% company paid premiums
- Unlimited Paid Time Off
- 20 weeks of 100% paid Parental Leave (paternity and maternity)
- 401(k) plan with 6% employer matching
- Generous company matching on donations to non-profit charities
- Up to $20,000 tuition assistance toward degree programs, plus up to $4,000/year for ongoing professional education such as industry conferences
- Plentiful snacks, drinks, and regularly catered lunches
- Dog-friendly office (CAM office)
- In-office gyms and showers (CAM, DC) or Equinox membership (LA, NYC)
- Stipend towards commuter or gym reimbursement
- Bike sharing program memberships
- Compassion leave and elder care leave
- Mentoring and additional learning opportunities
- Opportunity to expand professional network and participate in conferences and events
About Kensho
Kensho uses machine learning, artificial intelligence, natural language processing and data visualization techniques to solve some of the hardest analytical problems and create breakthrough financial intelligence solutions for our parent company, S&P Global.
Kensho was founded in 2013 by Harvard & MIT alums and was acquired by S&P Global in 2018. Kensho continues to operate as a startup in order to maintain our distinct, independent brand and to promote our breakthrough, innovative culture. Our team of Kenshins enjoy a dynamic and collaborative work environment that runs autonomously from S&P, while leveraging the unparalleled breadth and depth of data and resources available as part of S&P Global. As Kenshins, we pride ourselves on maintaining an innovative culture that depends on diversity and inclusion.
We are an equal opportunity employer that welcomes future Kenshins with all experiences and perspectives. Kensho is headquartered in Cambridge, MA, with offices in New York City, Washington D.C. and Los Angeles. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
What We Do
At Kensho, we leverage S&P Global’s world class data to research, develop and implement leading AI and machine learning capabilities that drive fact-based, objective decision making. Data is at the heart of Kensho, and it’s our technology. We build and deploy solutions that make that data accessible, insightful, relevant and transformative.
Why Work With Us
We are a diverse and inclusive group of curious, highly accomplished engineers and business professionals who value collaboration, curiosity, and mentorship at all levels. At Kensho, swinging for the fences is considered a team sport, and every Kenshin’s unique perspective and experiences are valued.
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Kensho Technologies Teams
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.