Data Scientist and Enterprise Intelligence

Role Purpose

We are looking for a Data Scientist to transform historical and real-time workforce, learning, skills, competency, recruitment and organizational data into predictive and prescriptive intelligence. The candidate will work beyond descriptive reporting to identify patterns, predict outcomes, detect risks and recommend interventions.

Key Responsibilities

1. Conduct exploratory data analysis across workforce datasets.
2. Develop statistical and predictive models.
3. Identify correlations between training, competency, assessment and workforce performance.
4. Develop workforce segmentation models.
5. Build skill-gap and competency-gap models.
6. Develop recommendation methodologies.
7. Perform cohort and longitudinal analysis.
8. Develop anomaly detection models.
9. Build predictive models for workforce requirements.
10. Develop learning effectiveness models.
11. Measure relationships between learning interventions and competency improvement.
12. Perform feature engineering.
13. Develop model validation methodologies.
14. Define confidence levels and model performance metrics.
15. Collaborate with BI teams to operationalize analytical insights.
16. Collaborate with AI/ML engineers to productionize validated models.

Target Use Cases

The Data Scientist will contribute toward capabilities such as:

·        Skill Gap Prediction

·        Training Recommendation

·        Competency Prediction

·        Workforce Readiness Index

·        Learning Effectiveness

·        Assessment Intelligence patterns

·        Career & Skill Pathways

·        Recruitment Intelligence

Required Skills

• 3–5 years of Data Science experience.
• Python
• SQL
• Pandas / NumPy
• Scikit-learn
• Statistical modelling
• Machine learning fundamentals
• Regression/classification/clustering
• Time-series analysis
• Feature engineering
• Model evaluation
• Data visualization

Preferred Skills

PyTorch/TensorFlow, NLP, Recommendation Systems, Graph Analytics, Knowledge Graphs, HR Analytics, Learning Analytics, MLOps, Azure ML or equivalent.

Key Deliverables

Predictive Models, Custom Intelligence Models, Recommendation Algorithms, Skill-Gap Models, Statistical Studies, Feature Definitions, Model Evaluation Framework, Data Science APIs/Specifications