Data Scientist - Day Rate Contract

Senior Data Scientist – Day Rate Contract

GTM Experimentation & Causal Inference 

Overview 

We are seeking a Senior Data Scientist to lead experimentation and causal inference initiatives the organization. This role will focus on designing and deploying rigorous measurement frameworks that move beyond descriptive analytics toward defensible, decision-grade impact estimation. 

You will partner with GTM teams across Sales, Customer Success, Marketing, and Finance to quantify incremental impact, support decision-making to improve resource allocation, and elevate experimentation standards across GTM funnels. The ideal candidate combines strong statistical foundations with production-level fluency in Python and SQL and has a demonstrated ability to translate causal insights into operational decisions. 

Key Responsibilities 

Causal Measurement & Experimentation 

·      Design, implement, and evaluate randomized controlled trials (A/B, geo experiments, incrementality tests). 

·      Develop quasi-experimental frameworks (e.g., difference-in-differences, synthetic controls, regression discontinuity, instrumental variables, uplift modeling). 

·      Define and operationalize an experimentation roadmap across GTM, including hypothesis prioritization, pre-registration standards, guardrail metrics, and clear decision thresholds for launch, scale, or sunset. 

·      Establish best practices for experimental design, power analysis, and bias mitigation. 

Modeling & Analytics 

·      Design, build and productionalize causal and predictive models using Python. 

·      Develop reusable experimentation and inference toolkits. 

·      Conduct robustness checks, sensitivity analyses, and assumption validation. Also partner with analysts on those activities to accelerate project delivery. 

·      Partner with data engineering to ensure high-quality, analysis-ready datasets and successful model deployment. 

Data & Infrastructure 

·      Transform and organize complex structured and semi-structured data sources using advanced SQL.  

·      Construct scalable analytical datasets from large-scale transactional and behavioral systems. 

·      Collaborate on experimentation infrastructure, including randomization frameworks and measurement pipelines. 

·      Improve data instrumentation, logging, and tracking to enable defensible inference. 

Stakeholder Partnership 

·      Translate statistical findings into clear, decision-oriented recommendations. 

·      Influence GTM strategy through evidence-based resource allocation guidance. 

·      Educate cross-functional partners on experimental design, causal reasoning, and interpretation of causal inference analytics. 

Qualifications 

Required 

·      5+ years of experience and a graduate degree in data science, computer science, applied econometrics, statistics, or related quantitative field. 

·      Strong grounding in causal inference theory and applied methods. 

·      Advanced proficiency in Python (e.g., pandas, NumPy, statsmodels, scikit-learn; experience with causal libraries such as DoWhy, EconML, or similar is preferred). 

·      Advanced SQL skills with experience working on large-scale data warehouses. 

·      Experience designing and analyzing online or field experiments. 

·      Ability to communicate, justify and visualize complex statistical concepts for non-technical stakeholders. 

Preferred 

·      Experience in B2B GTM environments (SFDC and MarTech data, sales performance, pricing, marketing mix, lifecycle optimization, or growth experimentation). 

·      Familiarity with uplift modeling and heterogeneous treatment-effect estimation. 

·      Exposure to Bayesian methods or hierarchical modeling. 

·      Experience deploying models in production environments. 

·      Experience influencing executive decision-making through formal experimentation readouts or investment cases. 

What Success Looks Like 

·      Experiment design standards improve across Sales, Customer Success, and other GTM teams. 

·      Reusable measurement frameworks reduce ad hoc analyses and increase organizational rigor. 

·      Incrementality and experimentation become core decision-making inputs across GTM. 

·      Resource allocation decisions are guided by defensible causal estimates rather than correlational metrics. 

·      Clear linkage between experimental evidence and capital allocation decisions. 

Impact 

This role directly shapes how the organization measures success. You will influence how capital is deployed across channels, how programs are evaluated, and how growth strategies are validated. The mandate is not incremental reporting improvement, but a structural shift toward causal decision-making. 

For more information please call Michael on 01-6146058 or e:[email protected]