Machine Learning Engineer - NLP
San Francisco, CA - Remote
Full Time Mid-level / Intermediate USD 133K
Swish Analytics
Sports betting & daily fantasy predictions, tools, analytics, projections & optimized lineups for NFL, MLB, NBA & NHL on FanDuel, DraftKings & Yahoo...Company Overview
Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients.
Swish is seeking a talented NLP Engineer to join our team and help us push the boundaries of what is possible with machine learning in the field of sports analytics. As an NLP Engineer, you will be responsible for designing, implementing, and evaluating state-of-the-art NLP models and solutions that address real-world challenges in sports. You will work closely with our team of Data Scientists and Data Engineers to build and deploy NLP systems that extract valuable insights from various sources to include open source data, league/team data, and historical data, amongst others.
This position is 100% remote
Responsibilities:
- Design, prototype, build and deploy NLP systems that extract valuable insights from league data, historical matchups, open source data, etc.
- Experience turning raw, unstructured data with open-ended project guidelines, into actionable production-level products
- Design and implement machine learning models using Natural Language Processing (NLP) to analyze sentiment analysis around players to determine expected output
- Research and stay updated on the latest advancements in recommendation systems and NLP
- Maintain and promote best practices for software development, including deployment process, documentation, and coding standards
- Internet text source gathering and preprocessing using scraping, APIs, and link crawling
- Contribute to technical and product discussions, and share knowledge and ideas with colleagues across the company
Qualifications:
- Bachelor degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area
- Minimum of 5+ years of demonstrated experience developing and delivering effective machine learning models to serve business needs
- Minimum of 2 years of relevant experience building NLP systems from ideation to production
- Proficient in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
- Production experience with modern packages such as NumPy, SciPy, Scikit-learn, Pandas, Keras, Tensorflow, PyTorch, CNTK, Spacy, Gensim, NLTK
- Strong communication skills when discussing technical concepts with technical and non-technical colleagues
Base salary: $133,000-180,000
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.Tags: APIs Bayesian Chemistry Computer Science Engineering Keras Machine Learning Markov Chain Mathematics ML models Monte Carlo NLP NLTK NumPy Open Source Pandas Physics Probability theory PyTorch Research Scikit-learn SciPy spaCy Statistics TensorFlow Unstructured data
Perks/benefits: Startup environment
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