Data Scientist – Experimentation & Causal Inference

Company: Wizeline

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.

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