Advanced Data Scientist

Job description

Key Responsibilities

Mathematical Formulation: Translate ambiguous business problems into mathematically sound framework objectives and optimisation targets.

Production-Grade Engineering

Write clean, modular, and maintainable code using production-level design patterns to scale mathematical models.

Big Data Processing

Design and manage scalable data pipelines to process massive datasets efficiently for model training and inference.

Deep Learning & Vision Development

Build, train, and fine-tune complex neural networks across text, audio, and visual modalities.

Cloud Deployment

Architect and deploy models to cloud environments, leveraging distributed computing and robust cloud infrastructure.

Required Technical Skills & Competencies

• Tooling, Libraries & Software Engineering Core Language: Advanced proficiency in Python with a strict adherence to Object-Oriented Programming (OOP) principles, clean coding standards, and design patterns.

Machine Learning Libraries

Advanced proficiency in scikit-learn (sklearn) for data preprocessing, feature engineering, and baseline modelling.

Deep Learning Frameworks

Core expertise in PyTorch (preferred) or TensorFlow for building, customizing, and training deep neural networks from scratch.

Big Data Ecosystem

Experience with Apache Spark (PySpark) and the Hadoop Ecosystem (HDFS, Hive, MapReduce) for handling, transforming, and querying large-scale distributed datasets.

Cloud Architecture

Experience building and deploying scalable machine learning applications on major cloud platforms ( AWS , Azure , or GCP ).

Core Mathematics & First-Principles ML Foundational Math

• Solid foundation in Linear Algebra (eigenvalues, SVD, matrix decompositions), Multivariable Calculus (partial derivatives, gradients, Jacobians), and Probability Theory (Bayesian inference, probability distributions, expectation maximization).

Machine Learning

In-depth understanding of standard Machine Learning algorithms (Trees, Boosting, SVMs, GMMs) with the ability to explain the underlying loss functions and optimizations mathematically.

Deep Foundations

Thorough understanding of Multi-Layer Perceptrons (MLPs), mathematical derivation of backpropagation, hyperparameter initialization strategies (Xavier, He), optimization variants (Adam, RMSProp), and advanced regularization techniques (L1/L2, Dropout, Batch Normalization).

Advanced Natural Language Processing (NLP) Sequential Networks

• Hands-on experience with sequence modeling, including Word Embeddings (Word2Vec, FastText), RNNs, LSTMs, and GRUs.

Transformer Ecosystem

Deep structural knowledge of the Transformer architecture (Self-Attention math, Multi-Head mechanisms).

Pre-trained NLP Models

Experience implementing and fine-tuning encoder-only (BERT, RoBERTa) and decoder-only (GPT series) architectures.

Computer Vision (CV) & Document AI Spatial Networks

• Deep understanding of Convolutional Neural Networks (CNNs), feature map mathematics, pooling operations, and advanced CV backbones.

OCR & Document Processing

Proven track record building or customizing Optical Character Recognition (OCR) systems for complex text extraction pipelines.

Vision Transformers

Familiarity with the adaptation of attention mechanics to visual tasks (ViTs, Swin Transformers). Education & Experience

Qualifications

Education

Bachelor’s, Master's, or Ph.D. in a highly quantitative field (Mathematics, Statistics, Econometrics, Computer Science, Physics, or Operations Research).

Experience

4 to 6 years of industry experience working as a Data Scientist or Machine Learning Engineer with a portfolio of complex multimodal projects.