Data Scientist

Job Description For More Details Call Khushi hidden_mobile Key Responsibilities AI-Driven Analytics & Insight Generation Design and develop AI/ML models to automate insights, anomaly detection, forecasting, and trend analysis Build decision-intelligence systems that provide recommendations and next-best actions Implement natural language querying (NLQ) and AI-assisted data exploration features Business Intelligence & Data Modeling Design semantic data models optimized for BI and analytics use cases Create advanced KPIs, metrics, and business logic aligned with enterprise goals Enable self-service analytics for business users Machine Learning & Advanced Analytics Develop predictive, descriptive, and prescriptive models Apply time-series forecasting, clustering, classification, and regression techniques Build explainable AI (XAI) models for transparency and trust in insights Data Engineering & Pipelines Collaborate with data engineers to build scalable ETL/ELT pipelines Ensure high data quality, accuracy, lineage, and governance Work with structured and unstructured data from multiple business systems Crucial Data Experience Areas Beyond specific products, a data analyst must master thestateof the data: Feature Engineering: The ability to identify and create new variables (features) that contribute most to an AI model's accuracy. Structured & Unstructured Handling: Experience moving beyond simple spreadsheets to process unstructured text (via NLP) and image data. Data Labeling & Annotation: Experience using or managing labeling processes (e.g.,MonkeyLearnfor text) to provide "ground truth" for supervised learning. Real-time Data Streaming: Using technologies likeApache KafkaorAWS Kinesisto feed live data for immediate model responses. AI Product Development Partner with product managers and UX teams to integrate AI features into the BI application Translate business requirements into data science solutions Optimize models for performance, scalability, and real-time inference Model Deployment & MLOps Deploy and monitor ML models in production environments Implement model versioning, monitoring, and retraining strategies Use MLOps tools for automation, CI/CD, and lifecycle management Stakeholder Communication Present insights, models, and recommendations to technical and non-technical stakeholders Create data stories, dashboards, and executive-level summaries Support sales, marketing, finance, and operations teams with AI-driven insights Partner with product managers and UX teams to integrate AI features into the BI application Translate business requirements into data science solutions Optimize models for performance, scalability, and real-time inference Model Deployment & MLOps Deploy and monitor ML models in production environments Implement model versioning, monitoring, and retraining strategies Use MLOps tools for automation, CI/CD, and lifecycle management Stakeholder Communication Present insights, models, and recommendations to technical and non-technical stakeholders Create data stories, dashboards, and executive-level summaries Support sales, marketing, finance, and operations teams with AI-driven insights Familiarity with cloud platforms (AWS, Azure, GCP) Experience with APIs and microservices for AI integration AI & BI-Specific Skills Experience building AI-powered BI or analytics platforms Knowledge of NLP for natural language queries and insight generation Understanding of explainable AI (SHAP, LIME) Experience with real-time analytics and streaming data Soft Skills Strong business acumen and problem-solving mindset Excellent communication and data storytelling skills Ability to work cross-functionally in a product-driven environment High attention to detail and innovation mindset Preferred Qualifications Experience with Decision Intelligence platforms Knowledge of AutoML and AI copilots for analytics Experience with LLMs for BI applications (prompting, RAG, semantic search) Familiarity with data governance, security, and compliance standards Key Performance Indicators (KPIs) Accuracy and adoption of AI-generated insights Reduction in manual reporting and analysis time Business impact from predictive and prescriptive analytics Model performance, uptime, and explainability Career Growth Opportunities Senior Data Scientist AI Products Lead AI/BI Architect Machine Learning Engineer Head of AI & Analytics