Senior Data Scientist

TRKKN Senior Data Scientist, Data & Strategy
As a catalyst for innovative digital marketing ideas, what excites us at TRKKN is using a data-driven approach to take our clients' business to the next level.

Working for TRKKN means that you'll be part of an inspiring, energetic, and international team in a fast-growing tech environment. We offer the ideal workplace for anyone who wants to grow, learn, and has a strong desire for solving complex business challenges.

Being on the bleeding edge of marketing technology and innovation means you will have an unparalleled opportunity for learning, along with personal and professional growth: there's never a dull moment here.

Responsibilities
As a Sr. Data Scientist, reporting into the Head of Data & Strategy at TRKKN North America (NA), you'll focus on building scalable machine learning and AI products to help evaluate and optimize the business impact of our clients' marketing investment.

Whether working with our existing repository of products or building TRKKN's next-gen capabilities, you'll leverage your expertise in statistics and programming to develop and deploy scalable solutions within the cloud.

Role
Working as a leader within TRKKN globally, you will contribute directly to company growth goals by faithfully managing and executing robust and complex data science and engineering projects. As company headcount grows, you'll be expected to foster the development of junior employees, creating a roadmap for professional growth for a data science career at TRKKN NA. To be successful in this role, you'll:

• Act as Data Science Subject Matter Expert (SME) in the delivery and development of machine learning and AI products
• Deploy data products from our existing proprietary codebase and translate outcomes/findings in a digestible format for clients and internal stakeholders
• Design tech infrastructure and data pipelines (ETL, data automation, data querying, etc.) when necessary
• Aid agency leadership New Business efforts by executing ad hoc and custom analyses when necessary

Requirements

• Bachelor's degree in a quantitative field (advanced degree is preferred)
• 5+ years of experience, preferably in Data Science
• Expertise with Google Cloud Platform's distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc)
• Experience with Git, GitLab, CI/CD pipelines on Gitlab, and Terraform
• Strong analytical mindset with expert knowledge of SQL and BigQuery, as well as BI tools such DataStudio and Looker (LookML) to implement business reports and BI solutions.
• Deep understanding and experience with advanced analytics, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) or Computer Vision (at least 3 of these areas)
• Proficiency in Python and specialized ML libraries (e.g. TensorFlow, Keras, PyTorch, scikit-learn, SciPy, Pandas, NumPy)
• Experience working on a data science team, participating in scrums, sprints, and retrospectives
• Ability to clearly explain complex ideas to non-expert audiences
• Familiarity with media, media measurement, predictive modeling, and segmentation a plus

This is the pay range the Company believes it will pay for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on relevant experience, other job-related qualifications/skills, and geographic location (to account for comparative cost of living). The Company reserves the right to modify this pay range at any time. For this role, benefits include: health insurance, vision insurance, dental insurance, 401(k), Healthcare Flexible Spending Account, Dependent Care Flexible Spending Account, vacation days, sick days, personal days, paid parental leave, paid medical leave, and STD/LTD insurance benefits.

Compensation Range

$140,000—$150,000 USD

This role is hybrid, requiring three (3) days per week in the office. The remaining two (2) days may be worked remotely. Specific in-office days will be discussed during the interview process, with flexibility to align with team needs. Please note that the number or required in-office days may be adjusted over time, potentially increasing the number of required in-office days based on business needs.