Data Scientist

Silverpush is at the forefront of AI-powered video advertising, delivering sophisticated video ad solutions that empower brands to achieve impactful campaigns within a privacy-centric environment. Operating across 30+ countries, we specialize in creating contextually relevant advertising experiences that drive genuine engagement and conversion. Silverpush's commitment to innovation and technological advancement enables us to navigate the evolving digital landscape, providing our partners with the tools necessary to connect with audiences on a global scale. We are dedicated to fostering a culture of creativity and excellence, driving the future of ad tech with integrity and foresight.

For more information about Silverpush’s innovative advertising solutions, please visit www.silverpush.co.

Responsibilities:

● Analyze complex datasets to identify trends, patterns, and correlations, and extract actionable insights that can inform strategic decisions.

● Design and build predictive models using statistical and machine learning techniques (e.g., regression, classification, XGBoost, clustering).

● Research and develop analyses and forecasting and optimization methods across ads performance, content performance modeling, and live experiments.

● Research and prototype using cutting-edge LLM technologies and generative AI to unlock new opportunities in personalization, targeting, and automation.

Ideal Candidate Profile

● 3+ years of experience in Data Science, ideally in advertising or media-related domains.

● Degree in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Masters in DS).

● Deep experience working with large-scale structured and unstructured data.

● Strong foundation in machine learning and statistical modeling.

● Familiar with building and deploying models in production (basic MLOps knowledge).

● Comfortable with NLP and computer vision, and interested in applying LLMs to real-world use cases.

● Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders.

Technical Skills

● Languages & Tools: Python, PySpark, SQL

● ML Techniques: Regression, Classification, Clustering, Decision Trees, Random Forests, XGBoost, SVM

● LLM Tech: Familiarity with tools like OpenAI, Hugging Face, LangChain, and prompt engineering

● Data Infrastructure: ETL tools, Postgres, BigQuery/Snowflake, S3/GCP

● Statistical Analysis: A/B testing, experiment design, causal inference