Aezion - Data Scientist - Predictive Modeling

Description :

Key Responsibilities :

This Oppourtnity is Work from Office in 5 days

Predictive Modeling & Analytics :

- Design and develop predictive models to identify :

- Quality performance trends and decline risks

- Compliance risk patterns and recurrence

- Burnout, attrition, or workforce risk indicators

- Apply statistical and machine learning techniques to structured and semi-structured data.

- Evaluate model performance using appropriate metrics and validation techniques.

Feature Engineering & Data Preparation :

- Partner with data engineering teams to define feature sets derived from interaction data, QA scores, sentiment, and operational metrics.

- Identify leading indicators and behavioral signals relevant to contact center quality and workforce health.

- Ensure data quality, consistency, and suitability for modeling.

Model Validation & Governance :

- Validate model outputs for accuracy, fairness, and interpretability.

- Ensure models are explainable and suitable for use in regulated environments.

- Document assumptions, limitations, and intended use of models.

- Participate in model review, approval, and recalibration processes.

Collaboration with AI & Platform Teams :

- Work closely with Solution Architects, AI/Prompt Engineers, and ML Engineers to integrate models into the platform.

- Support the deployment of models via Snowflake ML, SageMaker, or equivalent frameworks.

- Assist with defining monitoring, drift detection, and retraining strategies.

Insights & Business Interpretation :

- Translate analytical findings into actionable business insights.

- Collaborate with Product Owners and QA leaders to align models with business goals.

- Contribute to dashboards, reporting, and executive-level summaries.

Documentation & Knowledge Transfer :

- Produce clear documentation for models, features, and methodologies.

- Support audit readiness by providing transparent explanations of analytical outputs.

- Assist in knowledge transfer to operational and client teams.

Required Skills & Experience :

Technical Skills :

- Strong background in statistics, machine learning, and predictive analytics.

- Proficiency in Python and common data science libraries (e.g., pandas, scikit-learn).

- Experience working with large analytical datasets.

- Familiarity with cloud-based ML platforms (AWS SageMaker, Snowflake ML preferred).

- Ability to work with structured and semi-structured data.

Domain & Analytical Skills :

- Experience developing models in regulated or risk-sensitive environments.

- Strong analytical reasoning and problem-solving skills.

- Ability to balance model sophistication with interpretability.

- Exposure to contact center analytics, QA, or workforce analytics is a plus.

Soft Skills :

- Clear communication of technical concepts to non-technical stakeholders.

- Ability to collaborate in cross-functional teams.

- Strong documentation and presentation skills.

- Comfortable working in iterative, feedback-driven delivery models.

Nice-to-Have Qualifications :

- Experience with NLP features or sentiment analysis outputs.

- Familiarity with GenAI-derived signals and hybrid rule-based approaches.

- Experience supporting audit or compliance reviews.

- Healthcare, insurance, or financial services experience.

Success Metrics :

- Accuracy and stability of predictive models in production.

- Early detection of quality or workforce risks.

- Adoption of insights by QA, coaching, and leadership teams.

- Transparency and auditability of analytical outputs.

- Reduction in reactive QA and operational interventions.