Intermediate ML/NLP Data Scientist

Remote (United States or Canada)

Applications have closed

Paper

With personalized tutoring, enrichment programming, and college and career support, Paper’s Educational Support System helps all your students shine in school and beyond.

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Driven by the mission to democratize education, Paper is the largest provider of educational support, supporting millions of students through partnerships with thousands of school districts. Paper helps deliver true educational equity through their category leading Educational Support System (ESS) that offers virtual access to 24/7 tutors and essay reviewers. Founded in 2014, Paper philosophically believes that all students should be given the tools and resources to reach their academic potential, independent of socio-economic status, geography, language or other barriers. We are headquartered in Montreal, Quebec with remote employees across the US and Canada. Paper is proud to have been named by GSV as one of the most transformational growth companies in digital learning.

Paper is looking for an Intermediate Data Scientist - NLP to join the growing R&D team. You will be responsible for performing research and applying techniques in natural language processing, machine learning and information retrieval. 

In this role, you will be responsible for designing and implementing algorithms to solve technical problems and you are expected to adopt a scientific approach when conducting R&D experiments. you will spend a significant amount of time planning, building, and deploying NLP custom and off-the-shelf solutions while working closely with our engineers. You should be self-directed and comfortable conducting applied research in collaboration with a wide range of stakeholders and cross-functional teams. As an ideal candidate, you need to have a strong background in mathematics/statistics/computer science or a related field.

This position can be located in any geography in the US or Canada.

Responsibilities:

  • Develop, evaluate and deploy ML / NLP techniques.
  • Mine and analyze data from the company databases to create standardized datasets.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.
  • Assess the effectiveness and accuracy of new data sources and data collection techniques.
  • Use predictive modeling to improve service delivery, customer experience, and other business outcomes.
  • Develop custom data models and algorithms.
  • Coordinate with different functional teams to implement models and monitor outcomes.

Qualifications:

  • +2 years of hands-on experience building neural networks, statistical models and machine learning techniques, with a Master’s or PHD in Statistics, Mathematics, Computer Science or another quantitative field or +4 years of experience in ML development teams.
  • Extensive experience with Python and common data science libraries (Numpy, Scipy, Pandas, Matplotlib, Seaborn).
  • Thorough familiarity with machine learning libraries (Scikit-learn, TensorFlow, Pytorch) and NLP libraries (NLTK, spaCy, CoreNLP, Gensim, TextBlob).
  • Knowledge of a variety of machine learning techniques (supervised and unsupervised, clustering, regression, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Experience with putting Machine Learning models into production.
  • Experience with visualizing/presenting data for business stakeholders.
  • Strong problem solving skills with an emphasis on product development.
  • Excellent written and verbal communication skills.
  • A drive to learn and master new technologies and techniques.
  • Experience using graph learning is a plus.
  • Knowledge of GCP cloud computing services is a plus.

Job perks:

  • Work with a dynamic team that provides support whenever you get stuck.
  • Remote first environment.
  • Annual company-wide meetup.
  • Opportunity for career development with a fast-growing company.
  • A unique opportunity to make an impact by making education more equitable.
  • Stipend to help support the growth of your home office.
  • Unlimited access to tutoring for children of Paper employees.  

#LI-Remote #LI-ST01

About Paper

Paper offers an exciting, dynamic, inclusive work environment putting excellence at the center of everything we do. Our mission is woven into the fabric of our culture, challenging our team to build meaningful and creative solutions. 

We thrive when we collaborate with each other, and use integrity and selflessness to align our business decisions with our mission. We approach every challenge with positivity, achieving the outcome we want regardless of what gets in the way. Our tenacity propels our hyper-growth, where trust is key and we all strive to make an impact every day.

We believe that diverse teams build better products. Paper does not and will not discriminate on the basis of race, color, religion, gender, gender orientation, gender expression, age, national origin, disability, marital status, sexual orientation, or military status in any of its activities or operations.

Nobody checks every box, but the Paper team is built by passionate and innovative people who share our mission for democratizing education. If you don’t think you meet all of the requirements above but are still interested in the job, please apply.

PS. Equity is our mission! We make sure to treat all candidates equally: If you are interested please apply through our job board - our amazing talent team will reach out! Our team isn't able to pass on any calls/ emails our way - and this makes sure that the candidate experience is smooth and fair to everyone.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Clustering Computer Science GCP Machine Learning Mathematics Matplotlib ML models NLP NLTK NumPy Pandas PhD Predictive modeling Python PyTorch R R&D Research Scikit-learn SciPy Seaborn spaCy Statistics TensorFlow

Perks/benefits: Career development Equity Home office stipend Team events Unlimited paid time off

Regions: Remote/Anywhere North America
Countries: Canada United States
Job stats:  70  15  0

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