Data Scientist

Devxlabs; Singapore; Remote; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid model with WFH support

Health insurance (family)

Annual outings

Job summary

devx labs in Singapore is seeking an experienced data scientist to own the modelling layer for client engagements, turning customer, transaction and operational data into propensity, value, demand and pricing models that drive decisions across Customer Interactions and AI-Led Business Operations.

You'll frame the question, prove the signal exists, ship a production-ready model, monitor performance and interpret results for business stakeholders, ensuring measurable impact from deployment to

Qualifications

• Strong foundation in regression, classification, clustering, time-series and causal inference basics.
• Production modelling experience with at least two of: CLV, churn, propensity, segmentation, demand forecasting, pricing or uplift.
• Proficiency in Python and SQL; experience with cloud warehouses (BigQuery, Redshift, Snowflake or Databricks).
• Experience deploying models to production with monitoring, experiments, versioning and drift checks.

Responsibilities

• Frame business questions into modelling targets, labels, unit of analysis and success metrics.
• Perform EDA, data quality checks, feature engineering and leakage assessments on large datasets.
• Train and evaluate models across GBMs, GLMs, survival, clustering, time-series and DL where appropriate.
• Package models for batch scoring or API serving; monitor performance in production.
• Design and read A/B tests to demonstrate incremental business value.

Skills

Python

SQL

ML

Time-series

Deployment

MLOps

Explainability

Education

Bachelor's degree in Statistics/Math/CS/Engineering

Tools

BigQuery

Redshift

Snowflake

Databricks

MLflow

Airflow

Job description

devx Labs is an AI-native consulting companythat fundamentally reimagines business operations around AI capabilities. We don’t just implement AI tools - we transform how enterprises operate through first-principles thinking and outcome-driven execution.

About the role:

• You own the modelling layer of client engagements: turning customer, transaction and operational data into propensity, value, demand and pricing models that drive decisions across Customer Interactions and AI-Led Business Operations.
• Clients span retail, D2C, telecom, financial services and manufacturing. The problem changes every few months; the discipline doesn't: frame the question, prove the signal exists, ship a model that holds in production, show the business the number moved.
• Not a notebook-only role. You are accountable for the model from EDA through deployment, monitoring and the A/B read that proves it worked.

Problems you'll work on

• Customer Lifetime Value and customer health scoring
• Churn prediction, retention and win-back propensity
• Purchase, upsell and cross-sell propensity; Next Best Action
• Customer segmentation, behavioural clustering and persona generation
• Demand intelligence: SKU, store and channel forecasting, promo and seasonality effects
• Pricing and promotion optimisation: price elasticity, markdown and discount depth
• Uplift modelling and campaign targeting
• Anomaly detection and fraud or risk scoring
• Marketing effectiveness and attribution
• Recommendation and personalisation for commerce and CX

Responsibilities

Build and Ship Models - 60%

• Frame the problem:translate a business question into a modelling target, label definition, unit of analysis and success metric before writing code.
• Own the data work:EDA, data quality assessment, feature engineering and leakage checks on large transactional and behavioural datasets in SQL and Python.
• Build the model: select and train the right approach across classical ML (GBMs, GLMs, survival models, clustering), time-series forecasting and deep learning where it earns its place (sequence models, embeddings).
• Evaluate honestly: out-of-time validation, calibration, lift and gain curves, business-metric backtests, error analysis by segment. A good AUC on a leaky split is a failure.
• Deploy and operate: package models for batch scoring or API serving on Vertex AI or SageMaker, track experiments (MLflow or equivalent), monitor drift, data quality and performance decay in production.
• Prove impact: design and read A/B tests, uplift tests and holdouts to measure incremental business value, not just model accuracy.
• Explain the model: SHAP, partial dependence and segment-level diagnostics that make outputs defensible to business and risk stakeholders.

Drive Client Success - 30%

• Shape the approach: participate in discovery workshops, assess data readiness and define the analytics approach with Practice Leads and Solutions Consultants.
• Present with clarity: explain model results to client business and technology teams: what it does, where it is uncertain, how to act on it.
• Integrate: work embedded with client data and engineering teams to land models in their CRM, marketing automation, planning or pricing workflows.
• Iterate: track adoption and business outcomes post-launch; recalibrate as the business changes.

Multiply the Practice - 10%

• Build accelerators: feature libraries, evaluation templates, model cards and playbooks for repeat problems such as churn, CLV and demand.
• Mentor: guide DS 1 team members and interns on modelling rigour, code quality and client communication (DS 2 expectation).
• Champion AI-assisted development: use Claude Code, Cursor or similar to accelerate EDA, pipeline and test writing while owning correctness.

Requirements

The Scientist Mindset

• Rigour first: you distrust a result until you have checked the split, the leakage and the baseline.
• Outcome obsessed: success is retained customers, forecast error reduced and margin gained, not model count.
• Pragmatic: you know when a well-tuned GBM beats a transformer, and when it doesn't.

Technical Depth

• Statistics and ML fundamentals: regression, classification, clustering, time-series, hypothesis testing, causal inference basics (uplift, DiD, propensity matching). You can explain why a technique works, not just how to call it.
• Production modelling experience: hands-on with at least two of: CLV, churn, propensity, segmentation, demand forecasting, pricing or uplift. Kaggle-only or coursework-only experience doesn't clear the bar.
• Python and SQL: pandas, scikit-learn, XGBoost / LightGBM / CatBoost, statsmodels; strong SQL against cloud warehouses (BigQuery, Redshift, Snowflake or Databricks).
• Deep learning: working knowledge of PyTorch or TensorFlow for tabular, sequence or embedding use cases; judgement on when DL is warranted.
• Evaluation and testing: cross-validation strategy design, calibration, backtesting, unit tests for feature pipelines, validation of model outputs before release.
• Deployment and MLOps: has taken a model into production (batch or real-time) with experiment tracking, versioning and monitoring. Exposure to Vertex AI, SageMaker, MLflow, Airflow or equivalent.
• Data at scale: PySpark or Dask for large datasets; comfort with messy, multi-source enterprise data.
• Explainability and responsible AI: SHAP / LIME, fairness checks and clear model documentation.

Good to have

• LLM APIs or embeddings for feature enrichment, text analytics or agentic workflows.
• Optimisation and prescriptive methods: linear / integer programming, multi-armed bandits, reinforcement learning basics for pricing or allocation.
• Marketing mix modelling or Bayesian methods.
• Domain exposure to retail, D2C, telecom, BFSI or manufacturing.
• GCP Professional ML Engineer or AWS ML Specialty certification.
• Data storytelling: turn a confusion matrix into a business decision a CMO can act on.
• Workshop contributor: comfortable in discovery sessions, data assessments and solution reviews with client teams.
• Collaborative: works effectively with consultants, data engineers, AI engineers and client analysts.

Experience and Background

• DS 1: 2 to 3 years of hands-on data science experience with at least one model shipped to production.
• DS 2: 3 to 5 years of hands-on data science experience with multiple models shipped and owned end to end, including post-launch monitoring and iteration, plus some mentoring or project ownership.
• Education: Bachelor's or Master's in Statistics, Mathematics, Computer Science, Economics, Engineering or a related quantitative field.
• Stakeholder experience: consulting, product companies or startups where you worked directly with business stakeholders.
• Portfolio: GitHub repos, case studies or write-ups you can walk us through.
• AI tool credits and freedom to experiment with modern approaches.
• Performance-based appraisal at 6 months + annual appraisal cycle.
• Competitive pay with a structured CTC breakup.
• Work with clients across industries and domains.
• Hybrid model with work-from-home support and flexible working hours.
• No-cap leave policy — take time off responsibly, no fixed limits.
• Health insurance with spouse and kids coverage included.
• Yearly outings, and an open environment for innovation.

Reimagining customer experience, from first principles.

devx labs is an AI-native consulting firm that reimagines business operations around AI capabilities to deliver customer experience transformation for forward-thinking brands. We combine the unbiased perspective of young, AI-native talent with the seasoned judgment of experienced practitioners — solving for outcomes, not shipping deliverables for their own sake.

[ 01 ]

Customer Interactions

Reimagining every customer touchpoint across the entire journey.

[ 02 ]

AI-Led Business Operations

Optimizing downstream operations to enhance customer experience delivery.

[ 03 ]

The foundation that makes AI-native experiences possible.