Senior Customer Success Data Analyst

Job Description

Congratulations, you have taken the first step towards bagging a career-defining role. Join the team of superheroes that safeguard data wherever it goes.

What should you know about us?

Seclore is the Data Security Intelligence company for the era of enterprise AI. Seclore helps organisations secure the data that powers how people and AI work, enabling them to adopt AI safely without losing control of sensitive information. By securing data itself and ensuring protection travels with it wherever it moves, Seclore enables trust, control, and resilience across the modern enterprise. Seclore serves more than 500 global enterprises and government organisations across over 40 countries. For more information, visit www.seclore.com.

You would love our tribe

If you are a risk-taker, innovator, and fearless problem solver who loves solving challenges of data security, then this is the place for you!

A sneak peek into the role

The Senior Customer Success Data Analyst owns how Customer Success uses data to improve adoption, retention, drive expansion and decide where CSMs focus.

You will own the CS analytics strategy, metric framework, customer health score and churn-prediction approach, and act as the analytics partner to the Head of CS Operations and CS leadership. As the team is new, you will design how data from various platforms, databases and raw Excel dumps in Salesforce, Statisfy, Freshdesk and product telemetry comes together, and set the standard for how we use analytics and AI across Customer Success.

Here's what you will get to explore

CS analytics strategy

Own the CS analytics roadmap with the Head of CS Operations, prioritizing work by impact on retention and growth.

Act as the trusted analytics partner to CS leadership, shaping decisions on segmentation, coverage models and customer playbooks.

Present insights to CS leadership and contribute to board-level reporting.

Customer health and churn prediction

Own the customer health score methodology end to end — design, validation against actual renewal and churn outcomes, and regular recalibration.

Build predictive churn and propensity models (e.g. logistic regression, survival analysis) and turn them into early-warning signals CSMs can act on.

Compare retention and outcomes across customer groups (industry, region, account segments, products, sign-up period) to uncover what drives churn and growth.

Lead root-cause analysis of churn and downsell.

Metrics, dashboards and reporting

Define and govern the CS metric framework and a single source of truth so CS, Sales, Finance and Product report the same numbers and details.

Own reporting on core CS KRAs — Gross and Net Revenue Retention (GRR/NRR), other renewal and growth details, time-to-value, adoption and health distribution.

Oversee dashboards in Excel, presentations and Power BI for CS leadership and CSMs.

Lead data and meeting preparation for internal monthly and quarterly business reviews (MBRs/QBRs) and leadership/board reporting.

Product usage, renewals and expansion

Partner with Product and Engineering to model product telemetry for adoption and value-realization metrics.

Own risk-weighted renewal forecasting, so leadership sees a realistic outlook and CSMs know which renewals need attention.

Analyze feature adoption, usage depth and breadth, and onboarding milestones to identify at-risk and high-potential accounts.

Build models that identify upsell and cross-sell signals from usage, license utilization, account health and intelligence, and share them with CSMs.

Measure the impact of CS programs and plays on retention and expansion revenue.

Data foundations

Set data-quality and governance standards for CRM and CS-platform data, and drive fixes with system owners

Design the CS data model and integrations across Salesforce, Statisfy, Freshdesk, internal deployment tools and product data.

Document metrics, data sources and processes so analytics can scale with the team.

AI-enabled analytics

Use LLMs such as Claude hands-on to accelerate building modules, analysis of data, querying data, documentation and insight generation.

Design and roll out AI-assisted workflows and agents for CS — e.g. account summaries, sentiment analysis, renewal-risk briefs etc.

Set guidelines for responsible, secure use of AI with customer data, in line with Seclore's data-protection standards.

Your first 90 days

First 30 days

Understand our customers, products and CS motion; assess data sources across various tools like Salesforce, Statisfy, product telemetry etc.

Deliver a gap assessment of data quality.

Deliver a baseline view of retention and renewals.

By 60 days

Get the CS metric definition and framework agreed across CS and Finance.

Launch the first version of CS leadership dashboard.

Present the health-score framework and churn-model design.

By 90 days

Launch V1 of the health score.

Deliver a risk-weighted renewal forecast framework to CSMs for regular use.

Publish a prioritized CS analytics roadmap.

How we will measure impact over time: Predictive accuracy of the health score and churn models, use of insights in leadership and CSM decisions, data-quality improvements in CRM/CS platforms, and measurable contribution to GRR, NRR and adoption.

What you bring

5–7 years of experience in data analytics, with at least 2–3 years in customer success, revenue or business operations analytics, preferably in B2B SaaS or enterprise software.

Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Engineering or a related quantitative field; a master's degree is a plus.

Proven track record of building customer health scores or churn models end to end and getting them adopted by CS teams.

Advanced SQL and strong Python for data wrangling, statistical analysis and predictive modeling (e.g. logistic regression, survival analysis).

Expert-level dashboard building in Power BI and Excel.

Deep experience with Salesforce (or a sim