Lead 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 a Lead Data Scientist to join the growing R&D and Data Science team. In this role, you will be responsible for managing technical aspects of data science projects to ensure the success of delivered solutions. You will spend a significant amount of time designing, planning and executing technical solutions. You will work closely with our applied research scientists and engineers across the organization. You must be self-directed and comfortable providing guidance on applied research projects in Analytics and Machine Learning domains. You are eager to collaborate with a wide range of stakeholders and functional teams. The ideal candidate will 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:

  • Identify strategic technical objectives and lead data science technical roadmaps from ideation to implementation.
  • Improve the technical quality of the codebase via developing quality standards, leading training sessions, and enforcing code review processes.
  • Work with stakeholders throughout the organization to identify roadblocks and execute to unblock technical hurdles.
  • Mentor and coach data scientists on the team by providing direct feedback or comments in code reviews as well as leading pair sessions.
  • Lead rituals such as planning and retrospective meetings and backlog grooming to ensure consistent velocity.
  • Manage the team’s workload by controlling technical debt.
  • Collaborate with different functional teams and communicate technical and non-technical decisions.

Qualifications:

  • 5-7 years of experience in data analytics and building statistical models, with a Master’s or Ph.D. in Statistics, Mathematics, Computer Science or another quantitative field.
  • +2 years of experience leading highly technical and analytical teams. 
  • Proficiency in Python and Data science libraries including Numpy, SciPy, Pandas, Scikit-Learn, Tensorflow, Pytorch, MatplotLib and Seaborn (R and Matlab are a plus).
  • Experience manipulating data and drawing insights from large data sets.
  • Solid knowledge of production infrastructure and pipelines for Data science solutions.
  • Knowledge of a variety of machine learning techniques (supervised and unsupervised, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Experience working with and creating data architectures.
  • Knowledge of GCP cloud services.
  • Experience visualizing/presenting data for business stakeholders.
  • Strong problem-solving skills with an emphasis on product development.
  • A drive to learn and master new technologies and techniques.
  • Excellent written and verbal communication skills for coordinating across teams.

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.
  • 24/7 access to Paper for family members K-12.  

#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.

Tags: Computer Science Data Analytics GCP Machine Learning Mathematics Matlab Matplotlib NumPy Pandas Pipelines Python PyTorch R R&D Research Scikit-learn SciPy Seaborn Statistics TensorFlow

Perks/benefits: Career development Equity Home office stipend Team events

Regions: Remote/Anywhere North America
Countries: Canada United States
Job stats:  18  5  0

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