We’re looking for a highly skilled and innovative Advertising Data Scientist to lead the development of machine learning solutions that power our ad platform. You’ll work with rich datasets—from first-party driving data (e.g., GPS, driving events) to ad interaction data (e.g., impressions, clicks, conversions)—to build models that drive measurable business impact.

You’ll play a key role in shaping the future of our advertising technology by designing, prototyping, and productionizing advanced ML models. Your work will span the full lifecycle of optimization—from data collection and processing to modeling, experimentation, and KPI measurement.

Salary Range: $130,000 to $206,000; The salary may be negotiable based on experience, education, geographic location, and other factors.

What You’ll Do

Lead the design and implementation of ML models (traditional and deep learning) for large-scale advertising data.
Drive end-to-end development of optimization solutions including CTR prediction, dynamic bidding, pacing control, and more.
Collaborate with cross-functional teams including product managers, engineers, and analysts to bring models into production.
Conduct rigorous A/B testing and experimentation to validate model performance and business impact.
Communicate insights and recommendations clearly to both technical and non-technical stakeholders.
Champion best practices in data science including reproducibility, documentation, and peer review.

What We’re Looking For

Required Qualifications:

5+ years of experience in data science or machine learning, ideally in the advertising domain.
Deep understanding of digital advertising systems, auction dynamics, and measurement methodologies.
Hands-on experience with ad auction strategies, bidding algorithms, and experimentation frameworks.
Proficiency in Python and Spark for large-scale data processing and model development.
Strong knowledge of ML techniques including ensemble methods, deep learning, and reinforcement learning.
Experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, and Spark MLlib.
Proven ability to translate business problems into analytical solutions and deliver measurable results.

Preferred Qualifications:

Experience with control theory and its application to ad optimization.
Familiarity with cloud platforms (e.g., BigQuery, Redshift, Vertex AI, SageMaker).
Experience working with geospatial datasets (e.g., census data, POI, weather data).
Strong communication skills and a collaborative mindset.

Benefits offerings include but are not limited to:

401(k) with match
Medical insurance
Dental insurance
Vision assistance
Paid Holidays Off

To read our Candidate Privacy Information Statement, which explains how we will use your information, please visit https://www.akkodis.com/en/privacy-policy.

The Company will consider qualified applicants with arrest and conviction records in accordance with federal, state, and local laws and/or security clearance requirements, including, as applicable: