Data Scientist to $6500

Join MGID as a Data Scientist and become part of a team shaping the future of AdTech!
If you thrive on analyzing data, uncovering hidden patterns, and transforming insights into powerful solutions, this is your chance. We develop cutting-edge algorithms that drive the success of CPA  campaigns by achieving target metrics, increasing conversion volume, and maximizing budget efficiency. 

What You Will Do:

• Data Analysis: Explore data to identify trends, evaluate campaign performance, and support decision-making.
• Algorithm Development: Design, implement, and optimize algorithms for CPA optimization.
• Modeling and Optimization: Build and refine predictive models to improve decisions for CPA campaigns, ensuring alignment with business objectives.
• Stakeholder Collaboration: Work closely with product/business teams to translate technical solutions into measurable business impact.
• Experimentation and Validation: Conduct and evaluate A/B tests to assess the impact of algorithm changes.
• Visualization and Reporting: Create dashboards and visualizations to track key performance indicators (KPIs) and communicate findings to stakeholders.

Who You Are:

• Proactive problem-solving attitude: ability to independently analyze data, identify hidden issues, formulate hypotheses, suggest effective solutions, develop and test them.
• Ability to analyze and deeply explore data to extract valuable insights; ability to visualize and communicate those insights.
• Proven experience in data science, with a strong understanding of optimization problems.
• Proficiency in Python and SQL, including data manipulation and feature engineering.
• Expertise in machine learning techniques and tools (e.g., CatBoost, XGBoost, TensorFlow, PyTorch, scikit-learn).
• English - Upper-Intermediate.

Will be a plus:

• The ideal candidate holds a MSc or PhD in Engineering, Statistics, Mathematics or related fields
• Familiarity with the AdTech domain.
• Experience with big data technologies (Spark, Hadoop) and processing large datasets.
• Hands-on experience with A/B testing and business metric optimization.
• Background in working with high-frequency or large-scale event data (e.g., 15B+ events daily).