Job Description

Qualifications

? 4 + years in data science or machine-learning roles (NLP or time-series a plus).

? Hands-on with clustering, anomaly detection, and causal-inference techniques.

? Strong Python (pandas, scikit-learn, PyTorch/TensorFlow) and solid SQL.

? Experience turning notebooks into production jobs via Airflow, Flyte, or similar.

? Comfortable with vector databases and similarity search (Faiss, PGVector, etc.).

? You think in experiments, communicate results clearly, and love shipping incrementally.

Key Responsibilities

? Pattern Discovery - Build unsupervised / semi-supervised models (HDBSCAN, metric

learning, spectral, etc.) that group similar issues and surface them for human review.

? Trend Detection & Forecasting - Design change-point and anomaly detectors, then

forecast issue volume with Prophet, NeuralProphet, or your tool of choice.

? Cross-Channel Correlation - Link signals across chat, email, voice, and social to

reveal how pain points bounce between channels.

? Root-Cause & Prescriptive Modeling - Apply causal graphs or lightweight GNNs to

suggest likely drivers and remediation actions.

? Active-Learning Loops - Create feedback workflows that let analysts merge/split

clusters and continuously improve model accuracy.

? Experimentation & Metrics - Define success criteria, run controlled experiments, and

publish clear, visual results for the team.

? Collaboration - Partner with the LLM, platform, and dashboard engineers to deliver

end-to-end features--then measure the lift.

Job Type: Full-time

Pay: Up to ?1,000,000.00 per year

Benefits:

Provident Fund
Schedule:

Morning shift
Supplemental Pay:

Performance bonus
Experience:

machine learning: 4 years (Preferred) data science: 4 years (Preferred) python: 2 years (Required)
Work Location: In person