Data Scientist – Experimentation & Causal Inference

Data Scientist – Experimentation & Causal Inference

 

We are:

Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.

 

With the right people and the right ideas, there’s no limit to what we can achieve

Are you a fit?

Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

 

Key Responsibilities

• Design and run A/B tests and Randomized Controlled Trials (RCTs) to evaluate the impact of product and marketing initiatives.
• Apply causal inference techniques to estimate the true impact of business strategies.
• Partner with product, marketing, and analytics teams to turn business questions into measurable experiments.
• Analyze large datasets to uncover patterns, insights, and opportunities for optimization.
• Use tools such as Braze and Amplitude to evaluate campaign performance and user behavior.
• Build funnels, cohorts, and segmentation analyses to better understand user engagement.
• Communicate insights through clear reports and presentations for both technical and non-technical audiences.
• Maintain clear documentation of experiments, models, and analytical methodologies.

 

Must-have Skills

• 2–3+ years of experience in Data Science, Analytics, or a related field.
• Strong knowledge of statistics and experimentation methodologies.
• Experience designing and analyzing A/B tests or experimentation frameworks.
• Proficiency in Python or R for data analysis.
• Experience working with SQL and large datasets.
• Ability to communicate complex analytical findings to business stakeholders.
• Experience working in cross-functional teams in data-driven environments.

 

Nice-to-have:

• AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
• Experience with causal inference techniques (e.g., potential outcomes framework, difference-in-differences, causal modeling).
• Familiarity with experimentation and analytics tools such as Braze or Amplitude.
• Experience with big data environments such as Spark or Databricks.
• Exposure to cloud environments such as Azure.
• Experience in consumer-facing or marketing analytics environments.

 

What we offer:

• A High-Impact Environment
• Commitment to Professional Development
• Flexible and Collaborative Culture
• Global Opportunities
• Vibrant Community
• Total Rewards

 

*Specific benefits are determined by the employment type and location.

 

Find out more about our culture here.