58 Statistical Modelling Jobs - Page 3

Your day at NTT DATA The Senior Data Scientist is an advanced subject matter expert, tasked with taking accountability in the adoption of data science and analytics within the organization. The primary responsibility of this role is to participate in the creation and delivery of data-driven solutions that add business value using statistical models, machine learning algorithms, data mining, and visualization techniques. What youll be doing Key Responsibilities: Designs, develops, and programs methods, processes, and systems to consolidate and analyze unstructured, diverse big data sources to generate actionable insights and solutions for client services and product enhancement. Designs and enhances data collection procedures to include information that is relevant for building analytic systems. Responsible for ensuring that data used for analysis is processed, cleaned and, integrally verified and build algorithms necessary to find meaningful answers. Designs and codes software programs, algorithms, and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources Provides meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Directs scalable and highly available applications leveraging the latest tools and technologies. Accountable for creatively visualizing and effectively communicating results of data analysis, insights, and ideas in a variety of formats to key decision-makers within the business. Creates SQL queries for the analysis of data and visualizes the output of the models. Responsible for ensuring that industry standards best practices are applied to development activities. Knowledge and Attributes: Advanced understanding of data modelling, statistical methods and machine learning techniques. Strong ability to thrive in a dynamic, fast-paced environment. Strong quantitative and qualitative analysis skills. Desire to acquire more knowledge to keep up to speed with the ever-evolving field of data science. Curiosity to sift through data to find answers and more insights. Advanced understanding of the information technology industry within a matrixed organization and the typical business problems such organizations face. Strong ability to translate technical findings clearly and fluently to non-technical team business stakeholders to enable informed decision-making. Strong ability to create a storyline around the data to make it easy to interpret and understand. Self-driven and able to work independently yet acts as a team player. Academic Qualifications and Certifications: Bachelors degree or equivalent in Data Science, Business Analytics, Mathematics, Economics, Engineering, Computer Science or a related field. Relevant programming certification preferred. Agile certification preferred. Required Experience: Advanced demonstrated experience in a data science position in a corporate environment and/or related industry. Advanced demonstrated experience in statistical modelling and data modelling, machine learning, data mining, unstructured data analytics, natural language processing. Advanced demonstrated experience in programming languages (R, Python, etc.). Advanced demonstrated experience working with and creating data architectures. Advanced demonstrated experience with extracting, cleaning, and transforming data and working with data owners to understand the data. Advanced demonstrated experience visualizing and/or presenting data for stakeholder use and reuse across the business.

Roles & Responsibilities Displays structured problem solving, application of right tools & techniques to solve open ended problems, Creates productized analytics solutions or frameworks independently. Understand Business & product problems and come up with deep data backed solutions. Root cause analysis and Deep dive analysis of certain product problems Running and maintaining the reporting system, presenting insights at a weekly forum Building templates, dashboards in Excel or on the intranet for operational and management reporting Data extraction as per business team request for Ad hoc analysis Business analysis and understanding Evaluating metrics to be tracked as per business goals, exploring other available metrics for deeper understanding of product performance Mentor Data analysts on day to day analysis and upskilling Work in a fast-moving environment, across multiple projects with varying levels of complexity and detailing Skills and Experience: 3-5 years of experience working with large datasets and conducting quantitative analysis. Strong understanding of statistics and prior experience in building statistical models, including hypothesis testing, product experimentation, A/B testing, and regressions. Expertise in SQL and advanced proficiency in Python for data manipulation and analysis. Previous experience working with e-commerce funnels and retention analysis. Applied knowledge of widely used analytics techniques, as well as emerging or niche techniques. Ability to conceptualize and analyze product and business metrics to assess and optimize product features. Knowledge of business modeling and basic financial metrics (bonus). Quick learner with the ability to adapt to a dynamic work environment. Team player with strong interpersonal skills and comfort in collaborating with cross-disciplinary teams. Proven expertise in designing and building dashboards, identifying key metrics, and ensuring intuitive layouts; adept at using tools like UDP and Power BI following best practices. Bachelor s degree in Computer Science, Engineering, or equivalent from a reputed institution.

Responsibilities: Collaborate with business stakeholders to understand data requirements and objectives. Analyse complex data sets to extract valuable insights and trends. Interpret data to identify opportunities for process improvements and business growth. Develop reports, dashboards, and visualisations to communicate findings effectively. Conduct data quality assessments and ensure data integrity across systems. Support data-driven decision-making by providing actionable recommendations. Work with cross-functional teams to implement data solutions that meet business needs. Stay informed on industry trends and best practices in data analysis and business intelligence Qualifications: Bachelor's degree in Business Administration, Statistics, Computer Science, or related field. Proven experience as a Business Analyst or Data Analyst in a corporate setting. Proficiency in data analysis tools (e.g., SQL, Excel, Amazon QuickSight) and database querying. Strong analytical skills with the ability to translate complex data into actionable insights. Excellent communication and presentation skills. Experience with data visualization tools and techniques. Knowledge of business processes and operations. Preferred Skills: Familiarity with ETL processes and data warehousing concepts. Experience with predictive analytics and statistical modelling. Certification in business analysis or related field is a plus. Knowledge of Agile methodologies for project management.

Exp in SAS, SQL and large amounts of data US Stakeholder exp Exp of acquisition/account management credit risk models, transactional fraud models, marketing models, collections models, finance models, loss models, Loss forecasting (PD/LGD/EAD/CECL) Required Candidate profile SAS SQL Python Credit risk Credit Card Statistical Modelling Predictive Modelling

Required Skills (Technical):- Advanced knowledge of statistical techniques, NLP, machine learning algorithms and deep learning frameworks like TensorFlow, Theano, Keras, Pytorch. Knowledge of work with Pyspark is a must. (Working with Azure Databricks platform is preferred) Advanced knowledge in handling Time Series Data and modelling it with time series algorithms. Knowledge of working with Geospatial data would be preferable. Proficiency with modern statistical modelling (regression, boosting trees, random forests, etc.), machine learning (text mining, neural network, NLP, etc.), optimization (linear optimization, nonlinear optimization, stochastic optimization, etc.) methodologies. Build complex predictive models using ML and DL techniques with production quality code and jointly own complex data science workflows with the Data Engineering team. Familiar with modern data analytics architecture and data engineering technologies (SQL and No-SQL databases). Knowledge of REST APIs and Web Services 6. Experience with Python, R, sh/bash Required Skills (Non-Technical):- Fluent in English Communication (Spoken and verbal) Should be a team player Should have a learning aptitude Detail-oriented, analytically. Extremely organized with strong time-management skills Problem Solving & Critical Thinking Require Work Location :Remote , hyderabad,ahmedabad,pune,chennai,kolkata. Work Timing : 2:30 PM to 11:30 PM IST

Requirements 6-10 yrs of Strong experience with SAP UDF configuration and functional setup. Knowledge of statistical modelling concepts used in forecasting. Familiarity with integration to SAP CAR (Customer Activity Repository), F&R, or other planning tools. Good communication and documentation skills. Experience with large-scale retail forecasting systems is a plus. Responsibilities Gather business requirements and translate them into functional specifications for Unified Demand Forecasting. Configure and support SAP UDF modules including demand modelling, forecasting profiles, and integration with planning systems. Analyse demand signals and improve forecast accuracy through system tuning and configuration. Collaborate with business stakeholders, data science teams, and SAP technical teams. Conduct testing, support UAT, and document solutions. Monitor forecast performance and implement improvements as needed.

Data Science professional with a proven track record in training Engineering, IT, Diploma, Polytechnic and Technical candidates. With over a 7 yrs of experience in Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing, Specialise in delivering industry-oriented, hands-on training that equips candidates with the technical proficiency required in today's data-driven world.

Key responsibilities Work closely with the broader team do define and execute EDA road map Integrated solution for business based on data driven insights and strategies. Perform advance Statistical and Machine learning modelling exercise to develop descriptive/ predictive/ prescriptive models Perform data visualization and reporting for effective communication of insights generated by modelling exercises Develop basic database queries for data extraction Ability to communicate models and analysis in a clear and precise manner. Promote new learning and technique in the field of Data Science and its application in Insurance. Perform peer reviews and ideation sessions. Experience & Qualifications Bachelor and/or Masters in Statistics/Economics/Operations Research from Tier 1 colleges. Experience of 7-10 years of data science experience with alteast 4-6 years of experience in life insurance experience in model development, validation, and implementation with atleast 4 years of Insurance Experience Hands-on experience with R and/or Python for hypothesis testing, statistical modelling and Machine learning. Actuarial Exams is a plus Proven track record on algorithm development for business problems. Understanding of common algorithms like GLM, Decision Trees, Random Forests, XGBoost and Time Series Excellent communication and interpersonal skills, with the ability to explain complex algorithms to nontechnical stakeholders.