- Company Name
- Spendesk
- Job Title
- Data Scientist - Go To Market
- Job Description
-
**Job Title:** Data Scientist – Go‑to‑Market (GTM)
**Role Summary:**
Own end‑to‑end GTM data infrastructure and advanced analytics to enable revenue growth. Design pipelines, build predictive models, and deliver AI‑driven insights for Marketing, Sales, and Customer Success. Report to Revenue Operations and partner with senior GTM leaders to turn data into actionable revenue intelligence.
**Expactations:**
- Deliver reliable, scalable data pipelines and a unified GTM data model.
- Deploy production‑grade ML models for churn, lead scoring, forecasting, and recommendation.
- Provide executive‑level dashboards and automated reports that drive decision‑making.
- Ensure model monitoring, versioning, and continuous improvement.
- Collaborate cross‑functionally with data squads, AI engineers, and GTM stakeholders.
**Key Responsibilities:**
1. **Data Infrastructure & Engineering** – Design, build, and maintain GTM pipelines (lead capture → retention); enforce data quality, governance, and accessibility; create a single source of truth across marketing automation, CRM, product usage, and billing.
2. **Predictive Modeling & ML** – Develop churn, lead‑scoring, deal‑velocity, expansion, and LTV models using gradient boosting, survival analysis, deep learning, and time‑series methods; implement clustering for segmentation and run A/B tests/casual inference studies.
3. **AI Implementation & Advanced Analytics** – Productionize models with monitoring and version control; apply NLP to sales calls, tickets, and feedback; build recommendation engines and anomaly‑detection systems.
4. **Business Intelligence & Insights** – Create and maintain executive dashboards covering CAC, LTV, conversion rates, churn, NRR, NPS/CSAT, etc.; automate drill‑down reporting by segment, geography, and cohort; conduct statistical analyses, attribution modeling, and root‑cause investigations.
5. **Collaboration & Governance** – Work with Product & Tech data squads to define distributed governance, support real‑time reporting, and scale ML training; partner with VPs of Sales, Marketing, and Customer Success to translate insights into action.
**Required Skills:**
- Strong SQL and data‑engineering (Python/Scala, Airflow, dbt, Snowflake/BigQuery)
- Machine‑learning expertise: gradient boosting, deep learning, survival analysis, time‑series forecasting, clustering, NLP
- Model production & monitoring tools (MLflow, Kubeflow, Docker, CI/CD)
- Advanced statistics, experimental design, causal inference
- BI & visualization (Looker, Tableau, Power BI) and dashboard automation
- Excellent communication; ability to present complex insights to senior leaders
- Project management and cross‑functional collaboration
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative field (required)
- Master’s degree or PhD in a quantitative discipline (preferred)
- Professional certifications (e.g., AWS/GCP Certified Data Engineer, Google Cloud Professional Data Engineer, Microsoft Certified: Azure Data Scientist, or similar) are a plus.