2,836 Financial Analytics jobs in India

• Design, develop, and implement sophisticated statistical models and machine learning algorithms to analyze financial data.
• Extract, clean, and transform large datasets from various sources to prepare them for analysis.
• Develop and deploy predictive models for areas such as risk assessment, fraud detection, customer lifetime value, and market forecasting.
• Conduct in-depth exploratory data analysis to uncover hidden patterns and trends in financial behavior.
• Collaborate closely with financial analysts, portfolio managers, and business stakeholders to understand their needs and deliver data-driven solutions.
• Communicate complex analytical findings and recommendations clearly and effectively to both technical and non-technical audiences through visualizations and reports.
• Stay current with the latest advancements in data science, machine learning, and financial technology.
• Contribute to the development and maintenance of data infrastructure and tools.
• Mentor junior data scientists and promote best practices in data analysis and modeling.
• Evaluate and implement new data analysis tools and techniques.
• Ensure data integrity and adherence to security and privacy standards.
• Develop A/B testing frameworks and analyze experiment results.

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Economics, Finance, or a related quantitative field.
• Minimum of 5 years of progressive experience as a Data Scientist, with a strong focus on financial analytics.
• Proficiency in programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and R.
• Expertise in SQL for data extraction and manipulation.
• Deep understanding of statistical modeling, machine learning algorithms, and experimental design.
• Familiarity with financial markets, investment strategies, and financial modeling concepts.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent data visualization skills (e.g., Matplotlib, Seaborn, Tableau).
• Strong analytical, problem-solving, and critical-thinking skills.
• Exceptional communication and presentation skills, with the ability to explain complex concepts to diverse audiences.
• Proven ability to work independently and collaboratively in a remote team environment.

• Develop, test, and deploy advanced predictive models and machine learning algorithms.
• Analyze large, complex datasets to extract actionable insights and identify business opportunities.
• Design and implement data mining strategies for financial markets.
• Collaborate with stakeholders to define project requirements and deliverables.
• Communicate complex findings and recommendations to both technical and non-technical audiences.
• Build and maintain data pipelines and ensure data quality.
• Stay up-to-date with the latest advancements in data science and AI.
• Mentor junior data scientists and contribute to the team's knowledge sharing.
• Optimize model performance and scalability.
• Contribute to the strategic direction of data-driven initiatives.

• Master's or Ph.D. in Data Science, Statistics, Computer Science, or a related quantitative field.
• Minimum of 5 years of experience in data science, with a focus on financial applications.
• Proficiency in Python or R, including relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong experience with SQL and database management.
• Familiarity with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Deep understanding of statistical modeling, machine learning, and experimental design.
• Excellent problem-solving and analytical skills.
• Strong communication and presentation abilities.
• Ability to work independently and manage projects effectively in a remote setting.

• Develop and implement sophisticated statistical and machine learning models for financial forecasting, risk assessment, and fraud detection.
• Analyze large, complex datasets to identify trends, patterns, and correlations relevant to financial markets and customer behavior.
• Design and execute A/B tests and other experiments to evaluate the effectiveness of financial strategies.
• Build and maintain data pipelines for cleaning, transforming, and preparing data for analysis.
• Create compelling data visualizations and dashboards to communicate insights to stakeholders.
• Collaborate with cross-functional teams, including finance, product, and engineering, to drive data-informed initiatives.
• Stay abreast of the latest advancements in data science, machine learning, and financial technologies.
• Develop and deploy models into production environments, monitoring their performance and making necessary adjustments.
• Provide technical leadership and mentorship to junior data scientists.
• Communicate complex analytical results and recommendations clearly and concisely to diverse audiences.

• Design, develop, and implement sophisticated statistical models and machine learning algorithms for financial forecasting, risk assessment, fraud detection, and customer segmentation.
• Analyze large, complex datasets using advanced data mining and statistical techniques to uncover trends, patterns, and actionable insights.
• Collaborate closely with business stakeholders across finance, marketing, and operations to understand their needs and translate them into data-driven solutions.
• Develop and maintain robust data pipelines and ETL processes for efficient data extraction, transformation, and loading.
• Present complex analytical findings and recommendations to both technical and non-technical audiences in a clear and concise manner.
• Mentor junior data scientists and contribute to the growth of the data science team.
• Stay abreast of the latest advancements in data science, machine learning, and financial analytics, and champion their adoption where appropriate.
• Develop and implement A/B testing strategies to evaluate the impact of new models and strategies.
• Ensure the quality, integrity, and accuracy of data used for analysis and modeling.
• Contribute to the overall data strategy and roadmap of the organization.

• Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, or a related discipline.
• Minimum of 5 years of experience in data science or a similar analytical role, with a significant focus on financial services.
• Proficiency in programming languages commonly used in data science, such as Python or R.
• Strong experience with SQL and database management.
• Deep understanding of machine learning techniques, statistical modeling, and experimental design.
• Experience with big data technologies (e.g., Spark, Hadoop) is highly desirable.
• Excellent communication, presentation, and interpersonal skills, with the ability to explain complex concepts to diverse audiences.
• Proven ability to work independently and as part of a team in a fast-paced environment.
• Experience with data visualization tools (e.g., Tableau, Power BI) is a plus.
• Demonstrated success in delivering data-driven solutions that have had a measurable business impact.

The Data & Analytics team is responsible for integrating new data sources, creating data models, developing data dictionaries, and building machine learning models for Wholesale Bank. The primary objective is to design and deliver data products that assist squads at Wholesale Bank in achieving business outcomes and generating valuable business insights. Within this job family, we distinguish between Data Analysts and Data Scientists. Both roles work with data, write queries, collaborate with engineering teams to source relevant data, perform data munging (transforming data into a format suitable for analysis and interpretation), and extract meaningful insights from the data. Data Analysts typically work with relatively simple, structured SQL databases or other BI tools and packages. On the other hand, Data Scientists are expected to develop statistical models and be hands-on with machine learning and advanced programming, including Generative AI.

Key Responsibilities:

• Extract and analyze data from company databases to drive the optimization and enhancement of product development and marketing strategies.
• Analyze large datasets to uncover trends, patterns, and insights that can influence business decisions.
• Leverage predictive and AI/ML modeling techniques to enhance and optimize customer experience, boost revenue generation, improve ad targeting, and more.
• Design, implement, and optimize machine learning models for a wide range of applications such as predictive analytics, natural language processing, recommendation systems, and more.
• Implement advanced data augmentation, feature extraction, and data transformation techniques to optimize the training process.
• Deploy generative AI models into production environments, ensuring they are scalable, efficient, and reliable for real-time applications.
• Use cloud platforms (AWS, GCP, Azure) and containerization tools (e.G., Docker, Kubernetes) for model deployment and scaling.
• Create interactive data applications using Streamlit for various stakeholders.
• Conduct prompt engineering to optimize AI models’ performance and accuracy.
• Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence to bring innovative solutions to the team.
• Communicate complex findings and model results effectively to both technical and non-technical stakeholders.
• Continuously monitor, evaluate, and refine models to ensure performance and accuracy.
• Conduct in-depth research on the latest advancements in generative AI techniques and apply them to real-world business problems.

Qualifications:

• Bachelor's, Master's or Ph.D in Engineering, Data Science, Mathematics, Statistics, or a related field.
• 10+ years of experience in Advance Analytics, Machine learning, Deep learning.
• Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.G., Numpy, Pandas, TensorFlow, Keras, PyTorch, Scikit-learn).
• Experience with generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models (e.G., GPT-3/4, BERT, DALL·E).
• Understanding of model fine-tuning, transfer learning, and prompt engineering in the context of large language models (LLMs).
• Strong experience with data wrangling, cleaning, and transforming raw data into structured, usable formats.
• Hands-on experience in developing, training, and deploying machine learning models for various applications (e.G., predictive analytics, recommendation systems, anomaly detection).
• Experience with cloud platforms (AWS, GCP, Azure) for model deployment and scalability.
• Proficiency in data processing and manipulation techniques.
• Hands-on experience in building data applications using Streamlit or similar tools.
• Advanced knowledge in prompt engineering, chain of thought processes, and AI agents.
• Excellent problem-solving skills and the ability to work effectively in a collaborative environment.
• Strong communication skills to convey complex technical concepts to non-technical stakeholders.

Title: Finance Reporting (Sales/ Revenue)

Location: Mumbai

Educational Qualification: CA

Job Summary:

We are seeking a meticulous and results-driven Sales & Revenue Analyst to join our team. This role is crucial in supporting the sales function by preparing and analyzing key sales and revenue data, ensuring alignment with the Annual Operating Plan (AOP), and managing the reconciliation of purchase orders. The ideal candidate will have strong analytical skills, attention to detail, and the ability to collaborate cross-functionally with various teams, including Sales PMO, Account Management.

Key Responsibilities:

1. Preparation of Sales MIS (Management Information System):

• Validate order bookings in terms of commercials, subscription types, and IVG (internal classifications).
• Compare order data against the Annual Operating Plan (AOP) to ensure sales are on track.
• Collaborate with the Sales PMO team to ensure alignment with the sales strategy and targets.

1. Reconciliation of Purchase Orders:

• Reconcile purchase orders by interacting with the Account Management team to ensure all purchase orders are accurate and aligned with contractual terms.
• Address any discrepancies between sales orders and finance systems, ensuring smooth transaction flow.

1. Analysis of Order and Revenue Performance:

• Analyze sales performance by leaders and segments, focusing on order volumes and revenue generation.
• Track sales performance against the Annual Operating Plan (AOP) and identify any performance gaps or areas for improvement.

1. Sales Funnel Report Analysis:

• Review and analyze the sales funnel to track opportunities across different stages and product suites.
• Provide insights into the status of opportunities, helping prioritize efforts to convert leads into closed sales.

1. Validation of Customer/Partner Billing Methods:

• Validate customer and partner billing methods, payment terms, and conditions to ensure alignment with contractual agreements.
• Ensure accuracy in billing processes to avoid errors and disputes, contributing to smooth financial operations.

Key Requirements:

• Experience:
• 3+ years of experience in sales/revenue MIS operations, sales analysis, or a similar role in a fast-paced environment.
• Strong understanding of sales processes, reporting, and analytics.
• Skills & Competencies:
• Strong analytical skills with attention to detail and accuracy.
• Proficiency in Excel, MS Office Suite, and other data analysis tools.
• Familiarity with CRM systems (e.g., Salesforce, Microsoft Dynamics) is a plus.
• Ability to interpret and analyze sales data to make informed decisions.
• Strong communication and collaboration skills for cross-functional teamwork.
• Educational Qualification:
• Bachelor's degree in Business, Finance, Marketing, or a related field.

Advanced certifications (e.g., in sales operations or data analysis) are a plus.