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Director, Sales Analytics慨正橡扯 • London, England, United Kingdom
Postuler Maintenant
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Director, Sales Analytics

慨正橡扯
  • GB
    London, England, United Kingdom
  • GB
    London, England, United Kingdom
Postuler Maintenant

À propos

Hands-on Sales Analytics leader who turns Markets Sales growth opportunities into production analytics and AI/ML capabilities with clear KPI outcomes, strong governance, and senior stakeholder alignment.
Key role highlights
Commercial-impact role focused on measurable Sales outcomes (uplift, win-rate, pipeline velocity, wallet share, and coverage productivity).
Hands‑on expectation to code and prototype, contributing production‑grade analytics/ML components (not only oversight).
Own use‑case pipeline from idea to scaled adoption, with KPI definition, testing/experimentation, and benefits tracking.
Build on modern data/ML platforms (e.g., Databricks/Spark and Snowflake) with CI/CD, monitoring, and operational controls.
Operate in a controlled environment with strong model governance (model risk, compliance, and controls).
Partner with senior stakeholders across Sales, Product, Risk/Compliance, CDO/CTO, SMAD/Quants, and engineering to secure decisions and deliver outcomes.
Purpose of the role As Director – Sales Analytics, you will use data, analytics, and hands‑on AI/ML to deliver measurable commercial impact across Markets Sales (e.g., revenue uplift, conversion/win rate, pipeline velocity, wallet‑share growth, and coverage productivity). You will build a prioritised pipeline of high‑value use cases across the opportunity lifecycle (e.g., targeting, next‑best action, and coverage effectiveness) and take them from discovery through deployment and adoption using trusted data, strong model governance, and hands‑on engineering. You will lead senior stakeholders across Sales, Product, Risk and Compliance, partnering with CDO/CTO, SMAD/Quants, and engineering/data teams to align priorities, secure decisions, and deliver outcomes.
To enable data‑driven strategic and operational decision making through extracting actionable insights from large datasets, performing statistical and advanced analytics to uncover trends and patterns, and presenting findings through clear visualisations and reports.
Key responsibilities
Deliver Markets Sales commercial impact with hands‑on analytics and AI/ML (uplift, win‑rate, pipeline velocity, wallet share, coverage productivity).
Build a prioritised use‑case pipeline (targeting, next‑best action, coverage effectiveness) and ship to production with KPI definition and tracking.
Engineer end‑to‑end solutions, personally coding/prototyping critical components from data prep and features to modelling, productionisation, monitoring, and support.
Operationalise analytics/ML with trusted data, model governance, and delivery controls (CI/CD, deployment, monitoring) on Databricks/Spark and Snowflake.
Lead senior stakeholders (Sales, Product, Risk/Compliance, CDO/CTO, SMAD/Quants, engineering/data) to align priorities, secure decisions, and deliver outcomes.
Essential candidate skills
Demonstrated ability to deliver analytics and AI/ML end‑to‑end, writing production‑grade code from problem framing through build, deployment, and adoption.
Demonstrated ability to engineer trusted data and features (quality, lineage, reusable metrics) using Python/SQL on Databricks/Spark and Snowflake.
Demonstrated ability to apply engineering discipline to analytics/ML (Git, automated testing, code review, and CI/CD) to ship reliable changes.
Demonstrated ability to prioritise use cases with clear KPIs and run experiments that evidence commercial impact.
Demonstrated ability to influence senior stakeholders and deliver at scale within governance (model risk, compliance, and controls).
Desirable skills (optional)
Markets Sales analytics use cases (targeting, next‑best action, coverage effectiveness, pipeline) plus market data and pre/post‑trade analytics; Kafka and dbt exposure a plus.
Hands‑on coding in Python, SQL, and PySpark for pipelines and production analytics/ML; Java/C++ or kdb+/q a bonus.
MLOps in a controlled environment: MLflow, registry/versioning, CI/CD (GitLab/Jenkins), drift/performance monitoring, documentation.
Data governance practices and tooling: data quality checks, lineage/metadata, access controls, and privacy‑by‑design (e.g., fine‑grained controls such as Immuta or equivalent).
Advanced analytics/AI (incl. GenAI where appropriate) for decision support, recommendations, or productivity.
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  • London, England, United Kingdom

Compétences linguistiques

  • English
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