Key Responsibilities

1. Data Engineering & Raw Data Processing

Collect, process, and analyze large-scale raw datasets.
Work with numerical, transactional, time-series, textual, and image-based data.
Design and develop reliable data ingestion and preprocessing pipelines.
Perform data cleaning, transformation, normalization, and feature engineering.
Detect anomalies, missing values, and statistical inconsistencies.
Implement efficient data processing workflows using Python, Pandas, NumPy, and SQL.

2. Quantitative Research & Statistical Modelling

Apply probability theory, statistical inference, and mathematical modelling techniques.
Develop and analyze Gaussian and other probability distributions.
Conduct regression, correlation, and hypothesis testing.
Perform statistical forecasting and time-series analysis.
Identify patterns, trends, relationships, and anomalies in complex datasets.
Evaluate model assumptions, statistical significance, and predictive accuracy.

3. Machine Learning & Predictive Analytics

Design, develop, and optimize supervised and unsupervised machine learning models.
Implement classification, regression, and clustering algorithms.
Work with Random Forest, XGBoost, Gradient Boosting, and other advanced algorithms.
Build predictive models for financial analytics and quantitative research applications.
Perform model validation, hyperparameter optimization, and performance assessment.
Ensure model accuracy, reliability, interpretability, and reproducibility.

4. Deep Learning & Artificial Intelligence

Develop and implement neural network architectures using TensorFlow or PyTorch.
Work with CNN, RNN, LSTM, and Transformer models.
Build deep learning solutions for complex numerical and image-based datasets.
Explore anomaly detection and advanced pattern recognition techniques.
Apply computer vision and image-processing methodologies.
Research emerging AI technologies and their applications in financial analytics.

5. Google Cloud Platform (GCP) Specialisation

Practical knowledge of Google Cloud Platform is an important requirement for this role.

6. Data Visualization & Analytical Reporting

Create analytical dashboards and meaningful data visualizations.
Present complex numerical insights through clear and informative visual representations.
Identify statistical patterns, financial trends, and data relationships.
Prepare technical reports and maintain analytical documentation.
Communicate research findings and model performance to relevant stakeholders.

7. Model Deployment & Operational Excellence

Role: Data Science & Analytics - Other

Industry Type: Analytics / KPO / Research

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Analytics - Other