Data Scientist - Algorithms

Data Scientist - Algorithms

Remote/Telecommute JobREMOTE / Toronto, Ontario, Canada  - Permanent
This job allows you to work remotely 

Job Description

As a member of the Data Science - Algorithms team, you are primarily responsible for designing and building intelligent systems to extract knowledge and insights from data. Data scientists accomplish this by leveraging tools from predictive analytics, machine learning, artificial intelligence, and software engineering. The ultimate goal of the systems built by data scientists is to improve the Drop experience for members and partners, and create value for them.

Data scientists work closely with data engineers and infrastructure engineers to develop the infrastructure and tools needed to prototype and productionize data science pipelines. Data scientists also partner with Product, Engineering and Design to develop the product.

As a team, we value code quality, testability, scalable engineering design, and continuous process improvement. We leverage modern technologies, such as Airflow, Spark, Python, Redshift, Snowflake, Terraform, and various AWS services (EMR, Glue, DMS, and more). We maintain an efficient development environment to keep productive and rapidly innovate.

In this position you will:

• Apply data science and ML techniques against our large, secure store of financial transactions.
• Design and build ML pipelines with a product mindset to perform classification and prediction tasks related to entities in our platform such as users, brand, offers and rewards.
• You have a hypothesis-driven mindset combined with the drive to ship production quality solutions.
• Collaborate with internal stakeholders to identify problems, establish baselines, and design and build solutions that drive key product and business metrics
• Partner with Data and cross functional teams to build out Drop’s personalization roadmap.
• Manage end-to-end technical projects and collaborate with cross functional stakeholders to ensure the delivery of the project fulfills the product or business goal.
• Evaluate and identify open source technologies to apply ML that integrates seamlessly with Drop’s existing stack.
• Evangelize data science by fostering a culture of utilizing algorithms and educating employees across Drop on its impact.

Must Have Skills:

What you bring to the table:

• You have at least 3+ years of industry experience in a software engineering or Data Science role working with Python.
• You have experience building and managing large data pipelines and ETL flows to support Data Science solutions
• You have experience with cloud computing services (AWS, Google Clouds or Azure)
• You are comfortable working in different parts of the data stack and applying architectural patterns.
• You have experience building in a production environment using modern ML frameworks, and scalable data stores.
• You write testable and maintainable code to produce quality systems using engineering best practices.
• You thrive in a fast-paced environment; startup experience is not a strict requirement but a bonus. Drop welcomes people from all backgrounds and recognizes the value of diversity.
• You are passionate about building the next generation loyalty ML product to make life more rewarding.

Nice to Have Skills:

Bonus Points if:

• You’ve built scalable personalization systems.
• You’ve worked with Airflow, Postgres, Redshift, Docker and/or Kubernetes.
• You have experience with big data computing frameworks such as Apache Spark, Apache Hive, Presto.
• You have experience scaling architecture and working on products with millions of users.
• You’ve built financial, loyalty, or reward systems.
• You have a Master or PhD degree in Applied Computing, Computational Linguistics, Machine Learning or a related field.

Special Perks:


• Lifestyle Spending Accounts and Health Spending Accounts + drug, dental, travel, and group insurance coverage
• Flexible vacation + a work-anywhere-in-the-world program
• Parental leave benefits
• Stock options


Starting: ASAP
Dress Code: Casual

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