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Description
Job Title: Senior Data Scientist Location: Chennai/Hyderabad/Bangalore Experience Required: 8-12 Years Job Summary We are seeking an experienced Senior Data Scientist with a strong background in traditional Machine Learning (ML), AI, and expertise in Azure Fabric to work in the Finance Department of a Bank. The ideal candidate will play a key role in applying advanced analytics to drive business insights, improve processes, and enhance decision-making in the banking sector. The candidate should be proficient in ML models and AI technologies with a focus on real-world banking applications and have hands-on experience with Azure Fabric. Mandatory Skills Proven experience in traditional Machine Learning (ML) and Artificial Intelligence (AI). Strong experience in Azure Fabric and its integration with various banking systems. Expertise in Data Science methodologies, predictive modelling, and statistical analysis. Solid understanding of the Finance domain with a focus on banking processes and challenges. Hands-on experience with big data technologies and cloud platforms (Azure, AWS). Proficiency in Python-related data science libraries (e.g., Pandas, NumPy, Scikit-learn). Experience in data processing, ETL pipelines, and data engineering. Familiarity with SQL and NoSQL databases. Key Responsibilities Design and implement Machine Learning (ML) and Artificial Intelligence (AI) models to solve complex business problems in the finance sector. Work closely with business stakeholders to understand requirements and translate them into data-driven solutions. Develop and deploy ML models on Azure Fabric, ensuring their scalability and efficiency. Analyze large datasets to identify trends, patterns, and insights to support decision-making. Collaborate with cross-functional teams to integrate AI/ML solutions into business processes and banking systems. Maintain and optimize deployed models and ensure their continuous performance. Keep up to date with industry trends, technologies, and best practices in AI and ML, specifically within the finance industry. Qualifications Education: Bachelor’s/Master’s degree in Computer Science, Data Science, Engineering, or related field. Certifications: Relevant certifications in Data Science, Azure AI, or Machine Learning is a plus. Technical Skills Expertise in Machine Learning (ML) algorithms (Supervised and Unsupervised). Strong experience with Azure Fabric and related Azure cloud services. Proficient in Python, R, and data science libraries (Pandas, Scikit-learn, TensorFlow). Experience in AI and Deep Learning models, including neural networks. Working knowledge of big data technologies such as Spark, Hadoop, and Databricks. Familiarity with SQL and NoSQL databases. Experience with version control systems (Git, GitHub, etc.). Soft Skills Excellent problem-solving and analytical skills. Strong communication skills, with the ability to present complex data insights clearly to non-technical stakeholders. Ability to work effectively in a collaborative, cross-functional environment. Strong attention to detail and ability to manage multiple tasks simultaneously. A passion for continuous learning and staying updated on new technologies. Good to Have Experience in the banking or financial services industry. Familiarity with DevOps practices for ML/AI model deployment. Knowledge of cloud-native architecture and containerization (Docker, Kubernetes). Familiarity with Deep Learning and Natural Language Processing (NLP) techniques. Work Experience 8-12 years of experience in Data Science, with hands-on experience in ML, AI, and working within the finance or banking industry. Proven track record of designing and deploying machine learning models and working with Azure Fabric. Experience with client-facing roles and delivering solutions that impact business decision-making. Compensation & Benefits Competitive salary and annual performance-based bonuses Comprehensive health and optional Parental insurance. Retirement savings plans and tax savings plans. Work-Life Balance: Flexible work hours KRA (Key Result Areas) Timely and effective delivery of ML/AI models that solve complex business problems. Continuous improvement and optimization of deployed models. High-quality insights and data-driven solutions delivered for business stakeholders. Client satisfaction with AI/ML solutions implemented within the banking domain. KPI (Key Performance Indicators) Number of successful ML/AI models deployed and their performance post-deployment. Model accuracy and predictive capability (based on business goals). Client feedback on AI-driven solutions. Completion time for delivering actionable data-driven insights. Team collaboration and mentoring effectiveness with junior data scientists. Contact: hr@bigtappanalytics.com
