NLP Data Scientist
Posted: October 19, 2022
Are you a data scientist who’s eager to innovate and build scalable solutions that could support the next hit movie, TV show or video game? Is NLP your jam?
StoryFit is an Austin-based growth-stage startup that uses supervised machine learning and NLP to produce actionable data and audience insights that is changing the way entertainment clients acquire, develop, and drive growth and performance for their IP.
We are looking for an exceptional data scientist with extensive ML/NLP experience to work closely with our product team to develop and implement ML/NLP algorithms that impact the decision-making process for entertainment professionals. You will be responsible for building, measuring, and optimizing the quality of StoryFit’s algorithms. We are focused on high-impact projects utilizing big data analytics and machine learning to improve discovery, evaluation, and predictive outcomes.
We’re looking for someone who can:
- A motivated, results-oriented Data Scientist with strong rigor and demonstrable skills in experimentation, ML, NLP, data mining, and/or large-scale distributed computation and enjoy peering into the future and shaping its outcome
- Someone who is adept at making complex concepts simple and easy to understand, has exceptional technical writing skills, and is driven to show the world the power of applied analytics
Ideal Candidate Will Bring:
- Master’s degree or similar level experience in Data Science, Machine Learning, Mathematics, Computer Science, Statistics, or another relevant quantitative field
- 3+ years of experience as a Data Scientist/Machine Learning Engineer
- A strong knowledge of Natural Language Processing (NLP) including experience creating practical models solving problems like contextual emotion detection, converting text into a timeline, aspect-based sentiment analysis, stance detection, co-reference resolution, etc.
- Proficiency with Python (scikit-learn, SpaCy, NLTK, genism, AllenNLP, etc.)
- Experience with a deep learning framework (Tensorflow, Pytorch, etc.), deep learning architectures (RNNs, CNNs, Transformers, etc.) and platforms (e.g. Hugging Face)
- Experience with SQL, databases, and other data management tools
- A deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms