Digital Technology Specialist - Data Science

Digital Technology Specialist - Data Science Are you a highly motivated, creative individual and passionate about Data Science? Would you like to be a part of successful team?   Join our team!    The Data Scientist will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and consultancy development environment. In this role, you will contribute to the development and deployment of modern machine learning, operational research, semantic analysis, and statistical methods for finding structure in large data sets. As a Digital Technology Specialist, you will be responsible for: Develop analytics to address customer needs and opportunities. Work alongside software developers and software engineers to translate algorithms into commercially viable products and services. Work in technical teams in development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics. Perform exploratory and targeted data analyses using descriptive statistics and other methods. Work with data engineers on data quality assessment, data cleansing and data analytics Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes. Share and discuss findings with team members. Analyze large, structured, and unstructured datasets to identify trends, patterns, and opportunities. Develop statistical models to uncover insights and support business decisions. Perform exploratory data analysis (EDA) and hypothesis testing. Create dashboards and visualizations to communicate findings. Design, develop, train, and deploy machine learning models. Build predictive, classification, recommendation, and optimization solutions. Apply Generative AI, LLMs, Retrieval-Augmented Generation (RAG), and Agentic AI techniques where applicable. Evaluate model performance and continuously improve accuracy and reliability. Build scalable data pipelines for data ingestion, transformation, and feature engineering. Implement model deployment, monitoring, and lifecycle management using MLOps best practices. Work with cloud-native AI and data platforms. Ensure data quality, governance, security, and compliance standards. Collaborate with business stakeholders to understand requirements and define success metrics. Translate business problems into analytical and AI solutions. Present technical findings to both technical and non-technical audiences. Measure and communicate business impact of deployed solutions. Stay current with advancements in AI, machine learning, generative AI, and analytics. Prototype new approaches and technologies. Contribute to AI strategy, governance, and responsible AI practices.  Fuel your passion!    To be successful in this role you will have: Bachelor's or master's degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. Technical Skills Strong programming skills in Python and/or R . Expertise in SQL and database technologies. Experience with machine learning frameworks: Scikit-learn, TensorFlow, PyTorch, XGBoost.   Experience with cloud platforms: Microsoft Azure, AWS, Google Cloud Platform.   Knowledge of: Data Warehousing, Data Lakes, ETL/ELT pipelines, Spark and distributed computing.    Familiarity with MLOps tools: MLflow, Azure ML, Databricks, Kubernetes, Docker.    Experience with Generative AI, LLMs, Prompt Engineering, and RAG architectures. Strong understanding of: Statistics, Probability, Experimental Design, Time Series Analysis, Optimization Techniques.

Experience deploying AI solutions into production environments. Knowledge of Responsible AI, AI governance, and model explainability. Experience with vector databases (Pinecone, Azure AI Search, Weaviate, Chroma). Familiarity with AI agents and autonomous workflows. Industry knowledge in domains such as healthcare, finance, retail,