4 Regression Modelling Jobs

About the Role We are seeking an experienced Lead Engineer to drive the design, development, and scaling of AI-enabling platform services and public APIs that are secure, reliable, and cloud-native. In this role, you will not only be hands-on with technology but also take ownership of architecture decisions, technical strategy, and mentoring the Core AI Services team. You will work closely with cross-functional stakeholders to deliver innovative, production-grade AI capabilities at scale. Key Responsibilities Lead the design and delivery of scalable, fault-tolerant cloud-native services on Microsoft Azure. Own the development of secure, well-documented public APIs and SDKs for global developer consumption. Partner with product managers, data scientists, and architects to define and execute the AI platform roadmap. Drive architectural discussions, code quality, and best practices across the team. Embed robust security, compliance, and governance into all platform services. Mentor and guide engineers, enabling their technical and professional growth. Ensure end-to-end execution of AI solutions, from data pipelines and model deployment to orchestration and tooling. Champion innovation by rapidly prototyping and continuously improving AI-driven capabilities. AI & Cloud Expertise Proven experience with Large Language Models (LLMs), RAG, prompt engineering, agent orchestration, and AI tooling. Familiarity with emerging AI standards (MCP, A2A) and Responsible AI frameworks (NIST AI RMF, EU AI Act). Hands-on expertise with Python, PyTorch, TensorFlow, and AI orchestration tools (e.g., Semantic Kernel, AI Foundry). Experience leveraging AI-assisted development tools such as GitHub Copilot. Strong knowledge of cloud-native design patterns, microservices, and security-first development. Core Skills & Qualifications 12+ years of software engineering experience, with at least 3-5 years in a technical lead capacity. Strong proficiency in RESTful API architecture, lifecycle management, and security. Proven ability to lead distributed Agile teams and deliver high-quality enterprise-grade platforms. Track record of mentoring, guiding, and inspiring engineering teams. Excellent problem-solving, analytical, and communication skills with the ability to influence stakeholders at all levels. Candidates Willing to Relocate.

As a Senior Statistician at Rhythm, you will play a crucial role in leading statistical research, designing experiments, and developing predictive models to ensure robust data-driven insights. Your responsibilities will involve collaborating closely with data scientists, AI engineers, and business stakeholders to build scalable and interpretable statistical models that support Rhythm Innovations" AI initiatives. Your expertise in statistical methodologies and machine learning will be instrumental in enhancing the accuracy, reliability, and fairness of AI-driven solutions. Analyzing customer data to identify patterns, establish correlations, and derive actionable insights to enhance AI solutions will be a key aspect of your role. You will also develop and apply advanced statistical methodologies to analyze large-scale datasets and improve AI model accuracy. Designing and implementing rigorous experiments such as A/B testing, hypothesis testing, and Bayesian inference will be essential to validate AI models and business strategies. Collaborating with AI and machine learning teams to integrate statistical models into AI-driven applications will be a critical part of your responsibilities. Ensuring statistical integrity and robustness in AI solutions by developing methodologies for bias detection, uncertainty quantification, and explainability will also be a key focus area. Additionally, you will lead research initiatives on new statistical approaches to enhance AI fairness, reliability, and efficiency. Communicating complex statistical findings to both technical and non-technical stakeholders in a clear and impactful manner is an important part of this role. Staying up-to-date with the latest advancements in statistical methods, AI, and machine learning to continuously improve methodologies will also be expected. Moreover, mentoring junior statisticians and data scientists to foster a culture of statistical rigor and innovation will be a part of your responsibilities. The ideal candidate for this position should hold a Masters or Ph.D. in Statistics, Mathematics, Data Science, or a related field. A minimum of 5 years of experience in statistical modeling, experimental design, and data analysis, preferably in an AI-driven environment, is required. Proficiency in statistical programming languages such as R or Python, expertise in probability theory, Bayesian inference, regression modeling, and strong understanding of machine learning algorithms are essential qualifications. Experience working with large-scale data and cloud-based data platforms, proven ability to translate complex statistical concepts into actionable insights, excellent problem-solving and critical-thinking skills, as well as strong communication and collaboration skills are also required. Preferred qualifications include experience in AI ethics, fairness, and interpretability of machine learning models, knowledge of causal inference and advanced experimental design techniques, familiarity with deep learning frameworks like TensorFlow and PyTorch, as well as publications in peer-reviewed journals or conferences related to statistics, AI, or machine learning.,

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.

Collaborate with cross-functional teams to understand business requirements and develop AI-based solutions Utilize AI modeling and machine learning techniques to analyze data and create predictive and forecasting models Apply time series models to identify patterns and trends for proactive maintenance strategies Implement regression, logistic regression, decision tree, random forest, xgboost, bagging, and boosting algorithms to optimize predictive models Design and deploy industrial AI and IIOT solutions to enhance operational efficiency and reliability Utilize artificial neural networks, deep learning, computer vision, RNN, CNN, and reinforcement learning to develop advanced AI solutions Collaborate with experts to build recommendation engines and expert systems for AI-based applications Provide technical expertise and support to internal teams and external clients Stay updated with the latest AI technologies and contribute to the development of innovative solutions Communicate complex technical concepts to non-technical stakeholders in a clear and concise manner