Data Scientist
Toronto, Ontario, Canada - Remote
Applications have closed
Teramind
Comprehensive user behavior analytics software for insider threat management, data loss prevention, workplace productivity, employee monitoring & moreTeramind is a hybrid, global workforce building the next-generation Insider Risk Management and User Behavior Analytics platform.
Join our team of innovators who are redefining insider risk management through cutting-edge technology. More than 10,000 organizations across the globe have used' Teramind to mitigate insider threats and protect their sensitive company data with the most robust, enterprise-grade software on the market.
As a global team, Teramind embraces an inclusive and flexible work environment and team culture. We win together, learn from each other and respect each other while delivering best-in-class security solutions.
About the role
This is a fantastic opportunity to shape the uses of advanced analytics and ML at Teramind. We're creating a space where you can focus on stats/analytics and not be tasked with working in a fragmented environment making wrangling your main task.
The data scientist will have expert knowledge of advanced statistical concepts and machine learning (both traditional and neural network frameworks). You'll have demonstrated the ability to manage and deliver on multiple projects and a builder’s mindset with a willingness to question assumptions and conventional wisdom.
Your day-to-day
- Use computational and statistical methods for exploratory analysis to find patterns, anomalies, and deliver insights.
- Develop ML algorithms and various models.
- Translate a business need or question into a hypothesis that can be validated via experimentation or data analysis.
- Collaborate with software engineers and other stakeholders with the goal of productionizing and maintaining predictive, advanced analytics, and ML models.
- Design benchmarks, metrics, and monitoring to measure and improve services.
Requirements
- 5+ years developing and implementing machine learning/deep learning models such as XGBoost/CatBoost, CNNs, LSTMs, NLP frameworks, OCR, etc.
- Thorough understanding of MLOps best practices, ability to design, test, and measure algorithms/models
- Experience with open-source tooling and frameworks.
- Demonstrable experience in experimental design and statistical analysis - sample size calculation, hypothesis test/power analysis
- Passion for reasonable accuracy–explainability trade-offs in models
- Willingness to explain and defend employed models, their interpretation, and business value to the team and stakeholders
- Master’s degree OR Doctorate degree in a highly quantitative field (Computer Science, Statistics, Mathematics, Bioinformatics, Computational Biology, Microbiology, Software Engineering, Biological/Chemical Engineering)
Benefits
- Competitive salary with a focus on a global market.
- Career-growth opportunities.
- Flexible Time Off and Paid Time Off benefits.
- Ongoing training and development opportunities.
At Teramind, we’re a collaborative, forward-thinking team where new ideas come to life, experience is valued and talent is incubated. This is a remote job. Work from anywhere!
About our recruitment process
You can expect up to 4 interviews.
We don’t expect a perfect fit for every requirement we’ve outlined. If you can see yourself contributing to the team, we want to hear your story.
All roles require reference and background checks
Teramind is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration without regard to race, age, religion, color, marital status, national origin, gender, gender identity or expression, sexual orientation, disability, or veteran status.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Bioinformatics Biology Computer Science Data analysis Deep Learning Engineering Machine Learning Mathematics ML models MLOps NLP OCR Open Source Security Statistics XGBoost
Perks/benefits: Career development Competitive pay Flex hours Flex vacation
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