Data Science

Responsibilities

• Utilize advanced statistical methods and deep learning/machine learning techniques to analyze large datasets and extract valuable insights.
• Develop predictive models and algorithms to support business decision-making.
• Demonstrate expertise in the Biometrics domain or a similar industry, understanding the nuances of data and business requirements.
• Apply data science techniques to address specific challenges within the domain.
• Conduct effective feature engineering and data preprocessing to ensure high-quality input for machine learning models
• Good Knowledge of object detection and computer vision is required.
• Work with real-world data to derive meaningful features for analysis.
• Collaborate with cross-functional teams to define data requirements and deliver solutions that meet business objectives.
• Communicate findings and insights to both technical and non-technical stakeholders.
• Evaluate model performance using appropriate metrics and iterate on models for continuous improvement.
• Optimise/quantize models for scalability and efficiency in real-world applications, eg, edge devices.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and big data technologies for efficient data processing and analysis.
• Utilize version control systems (e. g., Git/Gitlab) to manage and track changes in code and models.
  

Requirements

• Exhibit strong programming skills in languages such as Python, R, or similar.
• Should have working knowledge of developing REST API's and frameworks like Flask/FastAPI.
• Implement and deploy machine learning models and contribute to codebase maintenance.
• Must have knowledge of deep learning model training, evaluation, and understanding of model inference.
• Bachelor's/Master's or Ph. D. in a relevant quantitative field such as Computer Science, Statistics, or Data Science.
• Overall 3+ years of industry work experience in computer vision, object detection, pattern recognition, artificial intelligence, automation, and/or vision processing.
• 3+ years of relevant experience as a CV Engineer, Data Scientist, Machine Learning Engineer, or related role.
• Experience with common languages (e. g., Python, SQL) and tools (e. g., TensorFlow, PyTorch, distributed training / inference) on-premises or cloud.
• Knowledge of CUDA, OpenCV, and Model Training on multiple GPUs.
• Proficient in at least one of: PyTorch (Preferred), TensorFlow, and Keras
• Coding experience in programming Languages: Python (must), C++
• Knowledge of text detection and OCR, human/face detection, generative models, video analytics, model compression/optimization.
  

• Statistics
• Computer Vision
• Regression/ Classification and Unsupervised Approaches
• Model Training, Deployment, and Evaluation
• Ensemble approaches
• Evaluation techniques
• Familiarity with various operating systems (e. g. Windows, UNIX) and databases (e. g. MySQL, NoSQL)
• Good team player and excellent written and verbal technical communication skills
• Experience with cloud platforms(AWS, GCP, Azure) and big data technologies is a plus.
• Experience with Docker, Kubernetes, Airflow/MLFlow is good to have.