Data Scientist

TELUS Digital AI Data Solutions; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

TELUS Digital AI Data Solutions in Bengaluru seeks a Data Scientist to embed with service delivery and client teams, turning large operational data into actionable decisions. You'll work at the intersection of data quality, human annotation, and model evaluation — measuring rater agreement and surfacing quality signals while building predictive and diagnostic models.

You will write SQL and Python, analyze rater data, design experiments, and present results to both technical and non-technical

Qualifications

• 4+ years of data science or quantitative analysis experience.
• Strong applied statistics including hypothesis testing, sampling, regression, and evaluation metrics.
• Proficiency in Python (pandas, scikit-learn, NumPy) and strong SQL; experience with large cloud data warehouses (BigQuery).
• Ability to translate ambiguous business problems into structured analyses and communicate findings clearly to stakeholders.

Responsibilities

• Analyze rater and annotation data to measure quality, consistency, and agreement.
• Build and validate statistical and ML models to predict quality issues and support capacity planning.
• Design metrics and experiments (A/B tests, sampling strategies, audit frameworks) to assess data and model quality.
• Frame business questions with delivery leads and client stakeholders to deliver actionable insights.
• Write efficient SQL and Python for analysis and pipelines in BigQuery or equivalent.
• Communicate results to technical and non-technical audiences via visualizations and narratives.
• Collaborate with data engineering and BI to productionize recurring analyses.

Skills

Data analysis

Statistical methods

Stakeholder communication

Problem framing

Education

Bachelor's degree in a quantitative field

Tools

Python

SQL

BigQuery

Looker

Job description

We're looking for a Data Scientist to embed with our service delivery and client teams and turn large, messy operational data into decisions. You'll work at the intersection of data quality, human annotation, and model evaluation — measuring rater agreement, surfacing quality signals, building predictive and diagnostic models, and translating findings into recommendations that leadership and clients act on. This is a hands on, business-facing role for someone who is as comfortable writing SQL and Python as they are presenting a trade‑off to a non-technical stakeholder.

What You'll Do

• Analyze rater and annotation data to measure quality, consistency, and agreement — including inter-rater reliability, precision/recall trade-offs, and temporal validation of task performance.
• Build and validate statistical and machine learning models to predict quality issues, detect anomalies, and support workforce and capacity planning.
• Design and evaluate metrics and experiments (A/B tests, sampling strategies, audit frameworks) to assess data and model quality for client programs.
• Partner with delivery leads, program managers, and client stakeholders to frame business questions, scope analyses, and deliver clear, actionable insights.
• Work with large datasets in BigQuery and GCP, writing efficient, well-tested SQL and Python for analysis and pipelines.
• Communicate results to technical and non-technical audiences through clear visualizations, concise narratives, and executive-ready summaries.
• Collaborate with data engineering and BI teams to productionize recurring analyses and move ad hoc work toward reusable, industrialized reporting.

What You Bring

Required

• 4+ years of experience in data science, quantitative analysis.
• Strong applied statistics: hypothesis testing, sampling, regression, and evaluation metrics (precision, recall, F1, agreement/reliability measures).
• Proficiency in Python (pandas, scikit-learn, NumPy) and strong SQL.
• Experience working with large datasets and cloud data warehouses (BigQuery or equivalent).
• Ability to translate ambiguous business problems into structured analyses and communicate findings clearly to stakeholders.
• Bachelor's degree in a quantitative field (Statistics, Computer Science, Mathematics, Economics, Engineering) or equivalent practical experience.

Nice to Have

• Experience with human-in-the-loop data, annotation quality, or crowd/rater workflows.
• Familiarity with GCP tooling (Dataflow, Cloud Run, Vertex AI) and modern data stack tools (dbt, Looker).
• Exposure to LLM/GenAI evaluation, model benchmarking, or data-for-AI programs.
• Experience with experimentation and causal inference.