Data Scientist - Amsterdam - Kinesso Data and Tech

• Collaborate with a global team of data scientists, engineers, and product managers to deliver media measurement and optimization products to hundreds of users.
• Apply Data Science, AI, ML, to image and video analysis, cross-campaign budget optimization, and bidding algorithms in a world of increasing media fragmentation and privacy-centric measurements.
• Build data science with engineering toolkits for job orchestration, MLOps, CI/CD, RESTful APIs, and scalable infrastructure.
• Scale data science across our agencies' client portfolio. Data science should be simple, easy-to-explain, and anchored in business value.

• Push the boundaries of machine-assisted human decision-making, for example, by automating optimization recommendations to enhance the decision-making ability of campaign managers and media investment teams.
• Foster a culture of innovation, encouraging the exploration of new techniques and technologies such as generative AI to enhance and scale our product capabilities.

• Move in lockstep with team members across Data Science, Product, and Engineering, ensuring clear and effective communication of product roadmaps, statuses, challenges, and successes.
• Collaborate closely with product teams to understand their needs and integrate AI/ML technologies into our products that meet those needs, driving value and efficiency.

• LLMs and Video annotation: Expertise in utilizing LLMs for semantic analysis of image and video content, coupled with proficiency in employing video annotation tools for detailed feature extraction and analysis, leveraging methodologies such as object detection and visual similarity comparisons. Experience with Vertex AI and Google Video Intelligence is a plus.

• Machine Learning: Expertise in machine learning, including regression, classification, and ensemble methods like Random Forest and Gradient Boosting. Skilled in model development and feature engineering. Experience with Deep Learning is a plus.
• Optimization: Proven familiarity and hands-on experience with optimization techniques and tools a big plus. Including but not limited to proficiency in software such as CPLEX, Gurobi, AMPL, GAMS or experience crafting custom optimization routines, employing methods such as stochastic gradient descent.
• Statistical Analysis: Knowledge of Bayesian modelling is preferred, while experience with Pyro is a plus. This may include hypothesis testing, A/B testing, synthetic control, time series forecasting, and backtesting.
• Python: Advanced knowledge of Python is essential (numpy, pandas,…), with the ability to write efficient, clean, and commented code for model and package development, automation processes and application development. Experience writing RESTful APIs is a plus, e.g., FastAPI.
• Data Management: Proficient in data manipulation and transformation techniques. Experience with SQL databases, ETL processes and frameworks (Dagster, Airflow, dbt,…), and data modeling as well as cloud-based data warehousing solutions like Snowflake or Google BigQuery. Understanding of data governance and quality control.
• MLOps: Experience in overseeing the life cycle of machine learning models from development to production. This includes observability, reproducibility and evaluation of both data and models. Experience with MLflow is a plus.
• CI/CD: Experience with agile development methodologies, such as Scrum or Kanban. Proficiency in automation tools and frameworks for continuous integration and delivery (CI/CD), and monitoring model performance in production environments.
• Cloud: Experience with cloud platforms (e.g. AWS, Azure, GCP) and containerization technologies like Docker and Kubernetes.

• 3+ years of hands-on industry experience in designing and productionizing machine learning models and AI applications preferred (2 years industry experience for candidates with a relevant PhD).
• Master degree or higher in a CS or quantitative discipline: Computer Science/Math/Statistics/Physics/Engineering.
• Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
• Experience with media optimization is a strong plus, especially in video or image optimization, real-time bidding and non-linear optimization algorithms. Practical experience in other industries, for example, financial time series, is welcome too.
• Demonstrated problem-solving and analytical thinking skills, with a focus on delivering practical and innovative solutions.
• Adaptability and willingness to embrace new technologies and challenges in a fast-paced and evolving environment.

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