3 Bayesian Methods Jobs nearby Pune

Role Overview: At Improzo, we are seeking a seasoned Deputy Manager/Group Manager in Advanced Analytics for the Lifesciences/Pharma domain. In this role, you will lead a dynamic team dedicated to providing advanced data analytics solutions to clients in Marketing, Sales, and Operations. Your proficiency in ML & DL Algorithms, NLP, Generative AI, Omni Channel Analytics, and Python/R/SAS will be essential for success. Key Responsibilities: - Partner with the Clients Advanced Analytics team to identify, scope, and execute advanced analytics efforts that address business questions, fulfill business needs, and contribute to business value. This may involve estimating marketing channel effectiveness or sales force sizing. - Maintain a comprehensive understanding of pharmaceutical sales, marketing, and operations to develop analytical solutions in these areas. - Keep abreast of statistical/mathematical/informatics modeling methodology to apply new methods effectively and to justify the selection of methods. - Develop POCs or R packages to enhance internal capabilities and standardize common modeling processes. - Independently lead and guide the team in implementing and delivering complex project assignments. - Provide strategic leadership by building new capabilities within the group and identifying business opportunities. - Contribute to whitepapers and articles at the BU and organization level to showcase thought leadership. - Deliver formal presentations to senior clients in both delivery and sales scenarios. Qualification Required: - Minimum 4-7 years of experience in data analytics. - Desired 2-4 years of relevant experience in Healthcare/Lifesciences/Pharmaceutical domain. - Proficiency in Python or R for statistical and machine learning applications. - Expertise in Regression, Classification Decision Trees, Text Mining, Natural Language Processing, Bayesian Models, and more. - Ability to build & train neural network architectures like CNN, RNN, LSTMs, and Transformers. - Experience in Omni Channel Analytics for predicting the Next Best Action using Advanced ML/DL/RL algorithms and Pharma CRM data. - Hands-on experience in NLP & NLG covering topic modeling, Q&A, chatbots, and document summarization. - Familiarity with LLMs (e.g., GPT, Lang chain, llama index) and open-source LLMs. - Hands-on experience in Cloud Platforms like Azure, AWS, GCP, with application development skills in Python, Docker, and Git. - Exposure to big data technologies such as Hadoop, Hive MapReduce, etc. - B.Tech/Masters in a quantitative discipline (Applied Mathematics, Computer Science, Bioinformatics, Statistics; Ops Research, Econometrics). Benefits: - Competitive salary and benefits package. - Opportunity to work on cutting-edge tech projects, transforming the life sciences industry. - Collaborative and supportive work environment. - Opportunities for professional development and growth.,

As the Director of Data Modelling and Insights at Mastercard, you will be responsible for partnering with the Product & Process Transformation team to drive deep analytical insights and build robust data models that enhance product execution and strategic decision-making. By leveraging your expertise in Excel, Power Query, and Power BI, you will transform data into meaningful dashboards, forecasts, and performance measures. **Key Responsibilities:** - Build and maintain robust data models in Power BI to support strategic decision-making for product and process initiatives. - Utilize Power Query to extract, transform, and clean large datasets from various systems. - Design and implement DAX measures to calculate metrics, track performance, and uncover key business insights. - Lead analytical deep dives into product velocity, pipeline friction, and development timelines using timestamp analysis. - Develop Bayesian forecasting models to predict outcomes such as launch timing, completion risk, or capacity gaps. - Construct crosstab analyses and experimental frameworks to evaluate the impact of interventions or process changes. - Define measurement frameworks and collaborate with teams to establish meaningful targets that drive accountability. - Work cross-functionally with product managers, engineers, and finance leads to integrate data thinking into everyday decision-making processes. **Qualifications Required:** - Bachelor's or Master's degree in Data Science, Engineering, Mathematics, Economics, or a related field. - Minimum of 8 years of experience in data analytics, business intelligence, or applied statistical modeling. - Expertise in Microsoft Excel, including Power Query and advanced functions. - Proficiency in Power BI, data modeling, DAX, and building interactive dashboards. - Demonstrated experience in applying statistical and probabilistic methods (e.g., Bayesian modeling) in a business context. - Strong communication skills with the ability to translate analytical findings into actionable recommendations. - Analytical problem solver with a passion for exploring patterns and making data-driven decisions. - Highly organized systems thinker who can simplify complexity into structured models and clear insights. - Proactive collaborator who excels in cross-functional environments and fosters trust through accuracy and timely delivery. This job at Mastercard emphasizes the importance of information security, and the successful candidate is expected to: - Adhere to Mastercard's security policies and practices. - Ensure the confidentiality and integrity of the accessed information. - Report any suspected information security violation or breach. - Complete all periodic mandatory security trainings as per Mastercard's guidelines.,

As a Data Scientist at Eaton Corporation's Center for Intelligent Power, you will play a crucial role in designing and developing ML/AI algorithms to address power management challenges. Your responsibilities will include: - Developing ML/AI algorithms and ensuring their successful integration in edge or cloud systems using CI/CD and software release processes. - Demonstrating exceptional impact in delivering projects, from architecture to technical deliverables, throughout the project lifecycle. - Collaborating with experts in deep learning, machine learning, distributed systems, program management, and product teams to design, develop, and deliver end-to-end pipelines and solutions. - Implementing architectures for projects and products, working closely with data engineering and data science teams. - Participating in the architecture, design, and development of new intelligent power technology products and production-quality end-to-end systems. Qualifications for this role include: - Bachelors degree in Data Science, Electrical Engineering, Computer Science, or Electronics Engineering. - 0-1+ years of practical data science experience, with a focus on statistics, machine learning, and analytic approaches. Required Skills: - Strong Statistical background including Bayesian networks, hypothesis testing, etc. - Hands-on experience with ML/DL models such as time series modeling, anomaly detection, root cause analysis, diagnostics, prognostics, pattern detection, and data mining. - Knowledge of data visualization tools and techniques. - Programming proficiency in Python, R, Matlab, C/C++, Java, PySpark, SparkR. - Familiarity with Azure ML Pipeline, Databricks, MLFlow, and software development life-cycle processes and tools. Desired Skills: - Knowledge of Computer Vision, Natural Language Processing, Recommendation AI Systems. - Understanding of traditional and new data analysis methods for building statistical models and identifying patterns in data. - Familiarity with open source projects like OpenCV, Gstreamer, OpenVINO, ONNX, Tensorflow, Pytorch, and Caffe. - Experience with Tensorflow, Scikit, Keras, Spark ML, Numpy, Pandas. - Advanced degree or specialization in related disciplines (e.g., machine learning). - Excellent verbal and written communication skills for effective collaboration with virtual, global teams. - Strong interpersonal, negotiation, and conflict resolution skills. - Ability to comprehend academic research and apply new data science techniques. - Experience working in both large teams with established big data platform practices and smaller teams where you can make a significant impact. - Innate curiosity and a self-directed approach to learning and skill development. - Ability to work effectively as a team player in small, fast-moving teams. This job opportunity offers a dynamic environment where you can contribute your expertise in data science to drive innovation in intelligent power technologies.,

As a Senior Data Quality Assurance Engineer at Morningstar, your role is crucial in ensuring the accuracy, integrity, and consistency of data across the systems. You will be responsible for designing and implementing quality frameworks, conducting data quality assessments, and leveraging AI/ML techniques to detect patterns and anomalies in financial and investment data. Your focus will be on automating data quality processes to uphold robust data governance and ensure compliance with industry regulations. - Lead the design and implementation of quantitative data quality frameworks, including statistical checks and anomaly detection systems. - Utilize advanced statistical methods to evaluate data quality across large, complex datasets. - Develop and integrate AI/ML models for predictive data quality checks and to improve data accuracy over time. - Ensure compliance with financial regulations and industry standards related to data governance. - Mentor junior quantitative analysts and promote best practices in data quality management and statistical analysis. - Communicate findings, data quality trends, and proposed solutions to senior leadership. - Lead the creation and maintenance of automated test scripts to improve test efficiency. - Ensure continuous integration of automated tests into the CI/CD pipeline. - Identify gaps in testing coverage and propose solutions. - Advanced knowledge of statistical methods, including linear/non-linear modeling, hypothesis testing, and Bayesian techniques. - Strong skills in applying AI/ML algorithms for data quality checks and predictive analysis. Experience with cloud-based environments (AWS, Azure, etc.). - Deep understanding of financial instruments, market data, and quantitative methods in portfolio management and risk analysis. - Proficiency in statistical programming languages (Python, R, SQL) and experience with tools like MATLAB, SAS, or similar platforms. - Experience in developing and implementing automated data validation processes, including real-time monitoring and alert systems. - Strong knowledge of data management principles, regulatory compliance, and data governance practices, particularly in the context of financial services. - Ability to mentor and guide junior team members, sharing expertise in statistical analysis, AI/ML, and data quality best practices. - Excellent analytical skills to identify root causes of data quality issues and implement long-term solutions. - Strong ability to work with cross-functional teams to enhance overall data quality.,