Data Scientist- Husky( India ) Guindy,Chennai

Title: Data Scientist- Husky( India ) Guindy,Chennai Id: 20970 Type: FullTime Location: Chennai, India At Husky TechnologiesTM, our success is based on your success. Our ability to keep our customers in the lead is based on building the strongest team possible. Husky TechnologiesTM has a strong foundation built on innovation, close customer relationships and a unique culture and values. We are dedicated to offering our customers the highest quality products and services and are looking for people with the inspiration and talent to develop with us as we pursue our ambitious growth strategy. We are a leader in developing state-of-the-art technology and it is this technology base that uniquely positions us to serve customers who seek differentiation through solutions that provide speed, flexibility and maximum productivity. This capability is at the core of our mission and competitive strategy. Husky TechnologiesTM offers a wealth of opportunity for personal growth and development. Most importantly, Husky Technologies TM offers an opportunity to work with – and be challenged by – a team of great people. Our success is possible because of the creativity, intelligence and passion of our people around the world and their desire to lead change. At the same time, we are not afraid to expect a lot and strive for leadership in all of our key markets. We are a company taking on new challenges and for the right people this means exceptional career development opportunities, the chance to be part of a team that is the best in the world at what we do and the experience that comes from working in an environment that demands constant transformation and innovation. Husky TechnologiesTM is an exciting company with tremendous potential. We have a great team and great expectations. If you are attracted to bold goals, believe in uncompromising honesty, support mutual respect, care about environmental responsibility, have a passion for excellence and a desire to make a positive contribution – then we want you to join the Husky TechnologiesTM team! Job Purpose The DST Data Scientist is a key member of Husky's global Enterprise Data & Analytics organization, responsible for transforming enterprise data into actionable business insights, predictive intelligence, and AI-driven solutions. This role partners with business stakeholders, data engineers, architects, and analysts to design, develop, and deploy advanced analytics, machine learning models, and enterprise reporting solutions. The Data Scientist leverages modern cloud-based data platforms including Azure, Databricks, Microsoft Fabric, Power BI, and Data Vault methodologies to support Husky's Enterprise Data Management strategy. The role contributes to the development of scalable analytical solutions that improve operational efficiency, product quality, customer experience, and strategic decision-making across the organization Key Responsibilities & Key Success Metrics Advanced Analytics & Data Science • Identify business opportunities and challenges that can be addressed through advanced analytics, machine learning, and artificial intelligence. • Design, develop, validate, and deploy predictive and prescriptive models. • Develop forecasting, anomaly detection, optimization, and classification models. • Apply statistical analysis and machine learning techniques to solve complex business problems. • Create reusable analytical assets and data science accelerators. Data Engineering & Data Preparation • Profile, assess, transform, and prepare enterprise data for analytical use. • Develop and maintain data pipelines using Azure Data Factory, Databricks, Microsoft Fabric, and SQL-based technologies. • Assist in the design and implementation of Enterprise Data Vault and Analytical Data Models. • Support data integration initiatives spanning ERP, CRM, Manufacturing, IoT, and external data sources. Data Visualization & Business Intelligence • Create analytical dashboards and executive reporting solutions using Power BI. • Translate complex analytical findings into business-focused recommendations. • Collaborate with business stakeholders to define KPIs, metrics, and reporting requirements. • Present analytical insights to technical and non-technical audiences. AI & Machine Learning Operations (MLOps) • Deploy and monitor machine learning models in production environments. • Implement model versioning, monitoring, governance, and performance management. • Support MLOps practices utilizing Databricks MLflow, Azure Machine Learning, Git, and CI/CD pipelines. • Ensure model explainability, reproducibility, and governance standards are maintained. Enterprise Data Management • Support Husky's Enterprise Data Management strategy and roadmap. • Contribute to Data Governance, Data Quality, Metadata Management, and Master Data initiatives. • Partner with Data Architects and Data Governance teams to ensure data consistency and compliance. • Support implementation of enterprise data standards and best practices. Stakeholder & Project Engagement • Facilitate requirements gathering and business analysis sessions. • Act as a consultant between business teams and technical delivery teams. • Participate in enterprise analytics and digital transformation initiatives. • Support project delivery through analysis, design, testing, deployment, and user adoption activities. Key Success Metrics Delivery Metrics • % of analytics projects delivered on time and within scope. • Number of production-ready analytical solutions delivered annually. • Reduction in manual reporting effort through automation. Data Quality Metrics • Data quality score improvement across supported domains. • Reduction in data-related incidents and defects. • Accuracy and completeness of analytical datasets. Business Value Metrics • Measurable business benefits generated from analytical solutions. • User adoption rate of dashboards and analytical tools. • Increase in business process efficiency supported by analytics. AI/ML Metrics • Predictive model accuracy and performance. • Machine learning model deployment success rate. • Percentage of models actively monitored through MLOps processes. Stakeholder Satisfaction • Business stakeholder satisfaction scores. • Reduction in reporting turnaround time. • Number of self-service analytics capabilities enabled. • Supporting program manager in delivery of the overall advanced analytical solutions • Ensuring that quality standards are met • Supporting Enterprise Data Management Strategy Qualifications Education • Bachelor's Degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or a related discipline. • Master's Degree considered an asset. Experience • 5-10 years of experience in Data Science, Analytics, Data Engineering, Business Intelligence, or Enterprise Data Management. • Experience delivering analytics and reporting solutions in manufacturing, industrial, or global enterprise environments. • Experience working with cross-functional international teams. Technical Skills Data Science & Analytics • Python • R • SQL • Machine Learning • Statistical Analysis • Predictive Modeling • Feature Engineering • AI/Generative AI technologies Cloud & Analytics Platforms • Microsoft Azure • Azure Data Lake Storage (ADLS) • Azure Data Factory (ADF) • Azure Synapse Analytics • Microsoft Fabric • Azure Machine Learning • Databricks Lakehouse Platform Databricks Expertise • Databricks Workflows • Delta Lake • Unity Catalog • MLflow • PySpark • Spark SQL • Structured Streaming • Databricks Asset Bundles Business Intelligence • Power BI • Power BI Service • DAX • Tableau (asset) • Semantic Models Database Technologies • SQL Server • Oracle • Snowflake (asset) • Fabric Warehouse • Azure SQL Data Architecture & Governance • Data Vault 2.0 • Kimball Dimensional Modeling • Enterprise Data Warehousing • Data Governan