Dun & Bradstreet Technology and Corporate Services India LLP is looking for candidate for the role of Analyst to support the Data Science team. The candidate needs to work closely with the team based in India and across a range of Analytics leaders who are located globally to fulfill the deliverables on a timely basis. Key ResponsibilitiesWork across range of Analytical Solutions that includes Design, Development, Validation, Calibration, Documentation, Implementation, Monitoring, and Reporting.Generate Analytical Insights on various customer portfolios.Develop Predictive Solutions using advanced statistical techniques to enable better decision making.Utilize latest data science techniques across both supervised and unsupervised machine learning methodologies, NLP, and development of new capabilities.Proficiency in using Machine Learning techniques.Collaborate with Technology teams to implement analytical solutions and models.Work with stakeholders in providing additional analysis based on specific needs.Work on newer strategic initiatives focused on LLMs and GenAI. Requirements2 to 5 years of relevant experience in Data Analytics / Data Science roles.Strong programming skills in tools such as Pyspark, Python, SQL to manipulate data and conduct statistical analysis.Knowledge of Python packages and GCP / AWS / Databrick.Sound Knowledge and proven experience on application of Statistical / Machine Learning technique.Experience in BFSI domain / Statistical Modelling & Validation is an added advantage.Ability to interpret and translate data into meaningful business insights.Good working knowledge on LLMs and GenAI would be desirable.Excellent verbal, written communication, and presentation skills.
As part of Dun & Bradstreet's Data & Analytics team, you will participate in all aspects of modeling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting. You will research complex business issues and recommend solutions, including model features, end products and any data required to support growing Dun & Bradstreet initiatives. Key Responsibilities:Utilize the latest data science techniques across both supervised and unsupervised machine learning methodologies, Natural Language Processing, and graph analysis in automating and scaling internal business processes.Establish and maintain strong relationships with key business stakeholders.Engage clients and D&B colleagues to identify business needs and develop, implement, and manage solutions.Strong communication skills and the ability to simplify and explain complex concepts for stakeholders, clients, and senior leaders.Participate in all aspects of a modeling engagement, including design, data requirements, development, validation, calibration, documentation, approval, implementation, monitoring and reporting.Develop Global Analytic Solutions inclusive but not limited to statistical models based on D&B’s established best practices, methodologies, and tools.Research complex business issues and recommend solutions, including model inputs and end products, focusing on addressing specific customer needs and use cases.Serve as a Subject Matter Expert on predictive models within the team and with business users; consult with the business, as appropriate, on predictive modeling solutions.Enjoy and share academic literature and industry best practices. Identify business relevance of new methods and work with cross functional teams to create prototypes, assist in creating business cases and go to market strategies.Validate the performance of existing quantitative risk models and recommend changes when necessary.Drive timely retrieval of risk analytics data from existing systems to create algorithms that meet business needs. Key Skills:Master’s Degree or Ph.D. in a quantitative/applied field preferred (Statistics, Econometrics, Computer Science, Operations Research, Mathematics, Engineering.7+ years’ operating successfully in data science roles, especially roles requiring cross-company collaboration and disciplined delivery of initiative.Hands-on experience applying modern machine learning technique.Ability to program in other statistical analysis languages, proficiency in programming languages (Python, R, SQL).Experience in feature engineering, automation, network analysis or Natural Language Processing.Experience working with outside clients on statistical engagements.Ability to manage multiple assignments, many of which have challenging timelines.Ability to work independently, as well as collaborate effectively in a team environment.Excellent communication and presentation skills.Proficiency in Microsoft Office Suite.Show an ownership mindset in everything you do. Be a problem solver, be curious and be inspired to take action. Be proactive, seek ways to collaborate and connect with people and teams in support of driving success.Continuous growth mindset, keep learning through social experiences and relationships with stakeholders, experts, colleagues and mentors as well as widen and broaden your competencies through structural courses and program.Where applicable, fluency in English and languages relevant to the working market.
Key Responsibilities: Work across range of Analytical Solutions that includes Implementation, Testing, Validation, Documentation, Monitoring, and Reporting. Extracting large data from various data sources using multiple tools such as Python/ Pyspark /Hadoop/ SQL/ etc., to perform multiple analysis. Creating data pipelines, performing model scoring, and validating model results. Utilize custom built automated frameworks for all deliverables. Work on MLOps frameworks to deploy, monitor and validate ML models in real time. Work with multiple teams such as Data Supply, ML Engineers, Technology to implement analytical solutions and models. Key Skills: 2 to 4 years of relevant experience in Data Analytics / Data Science roles. Strong programming skill in tools such as Pyspark, Python, SQL to manipulate data and create deployable production code. Knowledge of Python packages and tools. Sound Knowledge and exposure to application of Statistical / Machine Learning Techniques. Experience in building and optimizing data pipelines, architectures and data sets. Ability to interpret and translate data into meaningful business insights. Excellent verbal, written communication and presentation skills.