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.