360 Financial Data Analysis jobs in India

• Develop and implement sophisticated statistical models and machine learning algorithms for financial forecasting, risk assessment, and anomaly detection.
• Clean, process, and analyze large, complex financial datasets from various sources.
• Identify key business drivers and trends within financial data.
• Design and conduct A/B tests and other experiments to evaluate the impact of financial strategies.
• Collaborate with finance, product, and engineering teams to define data-driven solutions.
• Develop data visualizations and dashboards to communicate insights effectively.
• Stay abreast of the latest advancements in data science, machine learning, and financial analytics.
• Mentor junior data scientists and contribute to best practices within the team.
• Ensure data integrity and compliance with relevant financial regulations.
• Present findings and recommendations to senior management.

• Master's or Ph.D. in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
• Minimum of 5-7 years of professional experience in data science, with a focus on financial analytics.
• Expertise in programming languages such as Python or R, and proficiency with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
• Strong understanding of statistical modeling, machine learning techniques, and experimental design.
• Experience with SQL and working with large databases.
• Knowledge of financial markets, instruments, and concepts.
• Excellent analytical, problem-solving, and critical thinking skills.
• Superior communication and presentation skills, with the ability to explain complex technical concepts to diverse audiences.
• Proven ability to work independently and manage projects effectively in a remote setting.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.

• 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.

Role Name: Financial Reporting & Analytics Associate

Experience: 5-10 Years of Experience

Job Location: Remote

About us-

FEXLE- A Xoriant Company is a rapidly growing Salesforce consulting and development company with a strong track record of helping its clients achieve their business goals through Salesforce. FEXLE is known for its commitment to quality and customer satisfaction and its team of experienced and certified Salesforce professionals. It is a great place to work for professionals, looking for a challenging and rewarding opportunity.

Responsibilities-

• Conduct financial analysis to identify performance trends, key business drivers.
• Perform data interpretation and generate actionable insights to support management decisions.
• Build and maintain Tableau dashboards and automated reports for tracking KPIs and performance metrics.
• Prepare executive-level presentations and visual storytelling decks summarizing key findings.
• Collaborate with cross-functional and global teams to gather data, validate inputs, and present analysis.
• Ensure high accuracy, timeliness, and data integrity across all deliverables.
• Support ad-hoc analytical requests and continuous process improvements.

Requirements-

• Bachelor's degree in finance, Accounting, Business Analytics, or related field.
• 4-5 years of experience in BPO, Shared Services, or Analytics environment.
• Strong knowledge of financial metrics, forecasting, variance analysis, and performance tracking.
• Hands-on experience in Tableau (dashboard creation, visualization, storytelling).
• Advanced proficiency in MS Excel and PowerPoint.
• Excellent analytical thinking, attention to detail, and presentation skills.
• Strong communication skills to work with global and senior stakeholders.

• Lead and mentor a team of data scientists in developing and deploying advanced analytical models.
• Define the strategic direction for data science initiatives within the organization.
• Design, develop, and validate machine learning models for financial forecasting, risk assessment, fraud detection, and customer behavior analysis.
• Conduct complex statistical analysis on large datasets to derive actionable business insights.
• Collaborate with business stakeholders to identify analytical opportunities and translate business problems into data science solutions.
• Ensure the quality, integrity, and governance of data used for analysis.
• Communicate complex analytical findings and recommendations clearly to senior management and non-technical audiences.
• Stay abreast of the latest advancements in data science, machine learning, and AI.
• Champion best practices in data science methodology, coding standards, and model deployment.
• Contribute to the development of data infrastructure and tooling.

• Ph.D. or Master's degree in Computer Science, Statistics, Mathematics, Finance, or a related quantitative field.
• Minimum of 7 years of experience in data science, with at least 2 years in a leadership or senior role.
• Extensive experience in building and deploying machine learning models, particularly in the financial services industry.
• Proficiency in programming languages such as Python or R, and SQL.
• Strong experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Deep understanding of statistical modeling, hypothesis testing, and experimental design.
• Excellent analytical, problem-solving, and critical thinking skills.
• Exceptional communication and interpersonal skills, with the ability to lead and influence teams.
• Familiarity with data visualization tools (e.g., Tableau, Power BI).