Data Scientist

Data Scientist, AI & GenAI Solutions

Who we hire

Humble, hungry, smart. You are curious about how people behave, how businesses

operate, and how data can create measurable impact.

You enjoy turning ambiguity into insight. You challenge assumptions, ask better

questions, and look for evidence before reaching conclusions. You combine technical

depth with business understanding and communicate clearly with both engineers

and executives.

AI tools are part of your workflow, but scientific rigor drives your decision making.

We can put you in front of a client on week one.

The role

You build machine learning, advanced analytics, and GenAI solutions as part of a

small delivery pod.

You own major analytical workstreams end to end: problem framing, data

exploration, experimentation, model development, evaluation, deployment, and

business adoption.

This is business impact first. Model performance matters, but so does adoption,

explainability, reliability, and measurable outcomes.

You work directly with clients, not behind a ticket queue.

What a real week looks like

Build a churn prediction model and explain the drivers behind customer behavior.

Develop a eRAG-based GenAI solution using Vertex AI and Gemini models.

Evaluate multiple retrieval approaches and determine whether they are ready for

production.

Partner with Data Engineers to create scalable feature pipelines and analytical

datasets.

Present recommendations to client stakeholders and answer challenging questions

About Methodology And Outcomes.

Review experiments conducted by a teammate and improve the evaluation

framework.

Use AI tools throughout and catch the places where the AI is confidently wrong.

What you'll do

Frame business challenges as analytics, machine learning, or AI opportunities.

Perform exploratory data analysis and identify actionable business insights.

Build predictive, classification, clustering, forecasting, optimization, and

recommendation solutions.

Develop GenAI applications leveraging LLMs, RAG architectures, and agentic

workflows.

Evaluate models using rigorous statistical frameworks.

Run AI applications in production with monitoring, evaluation, and operational

controls.

Partner with engineering teams to deploy and scale machine learning solutions.

Present findings to executives and explain technical trade-offs in plain language.

Review analytical approaches, mentor Grade 8 team members, and raise the quality

bar across the pod.

Expertise required

Data science

Strong Python and SQL.

Feature engineering, exploratory analysis, model development, and evaluation.

Statistical reasoning and analytical problem-solving.

You can independently drive analytical initiatives from discovery to deployment.

Statistics and experimentation

Hypothesis testing.

Experimental design.

A/B testing.

Regression analysis.

Probability theory.

Statistical significance and confidence intervals.

You make decisions using evidence rather than intuition.

Machine learning

Classification and regression.

Clustering and segmentation.

Forecasting.

Recommendation systems.

Ensemble methods.

Model explainability.

You understand both algorithm selection and business applicability.

Google Cloud Platform And AI Platforms (Preferred)

Strong hands-on experience with:

 Vertex AI

 Gemini Models

 BigQuery

 Vertex AI Search

 Vertex AI Agent Builder

 Cloud Storage

 Dataflow

 Looker

 Looker Studio

You understand how to move solutions from notebooks into production

environments.

LLM engineering and GenAI

Prompt engineering.

Retrieval-Augmented Generation (RAG).

Tool-calling agents.

Context management.

Knowledge-grounded AI.

Agent orchestration.

Working knowledge of frameworks such as:

 LangGraph

 LangChain

 Google ADK

 Vertex AI Agent Builder

You understand when to use traditional ML, GenAI, or a hybrid approach.

MLOps and AI evaluation

Model monitoring.

Drift detection.

Experiment tracking.

Automated evaluations.

Prompt and model versioning.

Regression testing.

You can prove a solution improved outcomes, not merely claim it.

AI-assisted analytics

Copilot, Gemini, Claude, Cursor, or similar tools as part of your normal workflow.

Strong validation discipline.

Thoughtful use of AI to accelerate analysis without compromising rigor.

Communication

Executive storytelling.

Data visualization.

Client presentations.

Business problem framing.

You can explain a model to a non-technical executive without losing accuracy.

What a successful candidate looks like

You have 5 or more years of experience applying analytics, machine learning, or AI

to business problems.

You may have come from a product analytics team, a decision science organization,

a machine learning team, a startup, a consulting business, or a research

environment with strong business application.

You naturally discuss trade-offs:

"The more complex model improved accuracy by only 1%, but significantly increased

operational complexity, so we selected the simpler approach."

You talk openly about experiments that failed and what you learned.

You can describe your AI workflow in specifics.

You ask sharp questions before reaching a solution.

You give credit to teammates easily.

What success looks like

First 30 days: You understand the business context, contribute analytical insights,

and identify opportunities for machine learning or GenAI solutions.

First 90 days: You independently lead analytical workstreams, influence stakeholder

decisions, and contribute to productionalisation of AI initiatives.

At 12 months: You own analytical solutions that drive measurable business impact.

You have helped develop at least one Grade 8 professional. Clients know your name

and trust your recommendations.

Why join

Work on enterprise-scale AI, machine learning, and GenAI engagements for leading

global clients.

Build solutions using Vertex AI, Gemini, BigQuery, and the broader Google AI

ecosystem.

Work with engineers, architects, data engineers, behavioral scientists, and business

leaders on meaningful challenges.

Move beyond experimentation and deploy solutions that create measurable business

value.

Use the best AI tools to help shape how teams use AI responsibly and effectively.