Senior Data Scientist

Introduction:

As a Senior Data Scientist, you will play a crucial role in leading measurement initiatives, developing KPIs, and implementing analytics systems to drive business solutions for our customers. You will work closely with internal teams and clients to gather requirements, analyze data, and provide actionable insights.

Responsibilities:

• Lead measurement initiatives and develop KPIs for customers.
• Implement and maintain analytics systems and tools for efficient analysis.
• Design approaches for observational analytics and experimental methods.
• Consult clients on audience strategy, data requirements, and post-campaign measurement.
• Analyze customer data to drive insights and enable campaign optimization.
• Identify areas for improvement in effectiveness and productivity.
• Create presentations and communicate complex analytical concepts to various audiences.
• Build expert knowledge of data sources for enrichment and collaboration.
• Work directly with customer stakeholders to gather requirements and build relationships.
• Collaborate with product and engineering teams to support cloud infrastructure.
• Translate client needs into product improvements and design new offerings in the analytical space.

Requirements:

Required:

• Bachelor''''s degree with 10-15+ years of experience, Master''''s degree with 3+ years of experience, or PhD with 0-2 years of experience in a data science related field.
• Strong command of probability and statistics.
• Fluency in data and statistical analysis using SQL, Python, and PySpark.
• Strong command of data science toolkits (Pandas, NumPy, scikit-learn, etc.).
• Understanding of cloud platforms (AWS, Google Cloud Platform, etc.) and relational databases.
• Familiarity with randomized-controlled trials, A/B testing, and mar-tech/ad-tech measurement.
• Understanding of large-scale data analysis and optimization of Spark jobs.
• Knowledge of Business Intelligence tools for visualization.
• High emotional intelligence.

Preferred:

• Experience with machine learning algorithms and techniques.
• Knowledge of deep learning frameworks like TensorFlow or PyTorch.
• Experience with big data technologies such as Hadoop or Spark.
• Experience with data visualization tools like Tableau or Power BI.