Data Scientist Job

1. Job Summary

The Data Scientist will design, develop, test, and optimize advanced statistical models, machine learning systems, and AI-driven solutions to support TechVest Global’s enterprise clients. The role requires strong analytical capabilities, expertise in large-scale data processing, and the ability to translate complex data insights into actionable business recommendations. The candidate will work with structured and unstructured datasets, build ML/LLM/NLP models, automate workflows, and support end-to-end model deployment within cloud and MLOps environments.

2. Main Duties & Responsibilities (As Required Under NOC 21211)

The employee will perform the following duties:
A. Data Analysis, Modeling, and Experimentation
• Collect, clean, process, and structure large datasets for analytical and modeling purposes.
• Conduct exploratory data analysis, statistical modeling, and hypothesis testing to identify trends, correlations, and anomalies.
• Develop, test, validate, and optimize machine learning models, including classification, clustering, regression, neural networks, and advanced analytical techniques.
• Design and run large-scale experiments to evaluate model performance, identify relationships between variables, and generate robust insights.
• Implement new algorithms and methodologies drawn from current AI/ML research to address business problems.
B. NLP, LLM, and Deep Learning Model Development
• Build and refine advanced NLP and LLM-based solutions including sentiment analysis, named entity recognition, text classification, topic modelling, embedding generation, and conversational AI systems.
• Apply Transformer-based models (e.g., GPT, BERT) and fine-tuning frameworks (LoRA, PEFT) to enterprise use cases.
• Implement deep learning architectures (CNNs, RNNs, attention-based networks) for domain-specific applications.
C. AI/ML Engineering, Deployment, and MLOps
• Deploy scalable ML/LLM solutions using tools such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, GCP Vertex AI, AWS SageMaker/ECS/Lambda, and Kubernetes.
• Integrate models into production systems using FastAPI, Docker, and vector databases (Weaviate, Qdrant).
• Support end-to-end MLOps workflows including versioning, monitoring, optimization, and maintenance of deployed models.
D. Applied Research and Innovation
• Review and interpret technical research papers; rapidly prototype and implement research-grade solutions into production environments.
• Lead research activities involving structured and unstructured data, including Knowledge Retrieval, Information Extraction, and Knowledge Representation.
• Implement multi-agent orchestration frameworks (e.g., LangGraph Supervisor Google ADK) to design AI workflows for enterprise clients.
E. Business Intelligence, Reporting, and Visualization
• Transform complex analytical results into clear dashboards, visualizations, and business-ready insights.
• Prepare reports summarizing model outputs, key findings, and tactical or strategic recommendations.
• Collaborate with cross-functional teams to integrate data-driven solutions into business processes.
F. Collaboration & Stakeholder Engagement
• Communicate technical concepts effectively to non-technical stakeholders.
• Collaborate with software engineers, product managers, and client teams to define requirements and deliver high-impact solutions.
• Ensure compliance with data governance, privacy regulations, and internal quality standards.

Languages: English

Education: College diploma (1–2 years) or Bachelors in Computers, mathematics, statistics or equivalent experience in programming, Data Science, Computer Science, Statistics, Applied Mathematics, AI/ML, or a related field.

Experience: 4–6 years of relevant work experience as a Data Scientist with a strong focus on NLP, deep learning, LLMs, or enterprise-scale ML systems.

Technical Proficiencies
• Programming: Python, relevant ML/AI libraries
• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn
• AI/NLP/LLM Expertise: Transformers, RAG, re-rankers, generative AI, recommender systems
• Tools: LangChain, LangGraph, Hugging Face, ONNX
• Cloud: GCP Vertex AI, AWS SageMaker/ECS/Lambda
• MLOps & Deployment: FastAPI, Docker, Kubernetes
• Data Systems: Vector DBs (Weaviate, Qdrant)

Personal Suitability :Excellent communication, initiative, judgement, teamwork, attention to detail, adaptability, client focus.

Work Conditions: Fast-paced environment; work under pressure; tight deadlines; repetitive tasks; attention to detail; long periods of sitting.

Health/Dental Benefits, Paid Holidays, Vacations, and Sick Leave