Data Scientist

Job Description -

<>Candidate must have Causal inference experience.
causal inference experience refers to practical, hands-on expertise in identifying and quantifying cause-and-effect relationships using robust statistical and computational techniques beyond traditional machine learning methods that focus on prediction.</><>Data Scientist</><>Must have linkedin. </><> </><>Role Description</><>This is a remote role for a Data Scientist . You will work closely with product, and engineering teams to support data-driven decision-making through robust, production-grade causal modeling. This role requires hands-on experience deploying causal inference models at scale, and a strong academic foundation in causal reasoning.</><> </><>Responsibilities</>

• <>Design, implement, and productionize causal inference models</>
• <>Analyze experiments and quasi-experiments</>
• <>Collaborate with engineering to integrate models into production systems</>
• <>Communicate findings clearly through presentations, dashboards, and documentation</>
• <>Research new causal modeling techniques</>

<> </><>Minimum Qualifications</>

• <>Ph.D. or Masters in Statistics, Economics, Computer Science, or a related quantitative field</>
• <>1+ years industry experience post Ph.D, 3+ years industry experience for Masters</>
• <>Must have completed formal coursework in causal inference or econometrics at university level</>
• <>Experience with causal inference modeling and experimental design</>
• <>Solid knowledge of machine learning and statistical inference</>
• <>Strong communication skills for translating complex concepts to non-technical audiences</>
• <>Detail-oriented with excellent documentation and reporting skills</>
• <>Proficient in Python or R</>

<> </><>Preferred Qualifications</>

• <>Previous experience in a similar role is a plus</>
• <>Familiarity with model monitoring and MLOps tools</>
• <>Publications, blog posts, or open-source contributions related to causal inference</>
• <>Experience with state-of-the-art causal causal algorithms</>