3 Churn Prediction Jobs

You will join our digital transformation team as a CDP & Marketing Analytics Specialist, bringing your expertise in Customer Data Platforms (CDPs) and customer data management to drive data-driven decision-making and hyper-personalized marketing strategies at scale. Your responsibilities will include designing and implementing end-to-end customer data ingestion strategies, building real-time data pipelines, and developing AI/ML-driven customer segmentation models. Additionally, you will perform advanced analytics tasks such as CLV estimation, churn prediction, attribution analysis, sentiment analysis, and propensity modeling. Your role will involve designing composable CDPs using modern data platforms, deploying predictive models for automated customer service chatbots and content personalization, and implementing real-time decisioning engines for dynamic campaign execution. Collaboration with cross-functional teams and continuous monitoring and tuning of models and pipelines for optimal performance will be essential. To qualify for this role, you should hold a Bachelor's or Master's degree in computer science, Data Science, Marketing Analytics, or a related field with at least 5 years of experience in CDP implementation and marketing analytics. Proficiency in Python, SQL, and ML frameworks, as well as experience with tools like Azure Data Factory, Databricks, and Snowflake, are required. Exposure to MLOps practices, NLP techniques, and campaign attribution models will be beneficial. Preferred skills include prior experience in B2C/D2C industries such as retail, travel & hospitality, or e-commerce. If you have experience with tools like Hightouch, Segment, mParticle, or Salesforce CDP, proficiency in Python, SQL, and AI/ML frameworks, experience with Azure Data Factory, Databricks, Synapse, and Snowflake, and deploying GenAI applications, you are encouraged to apply. This is a full-time, permanent position requiring in-person work and offering the opportunity to leverage your expertise in CDPs and marketing analytics to drive impactful digital transformations.,

Chargebee is seeking a visionary and hands-on Director of Data Analytics, Science & AI Enablement to lead the creation and growth of a data function that powers enterprise-wide AI initiatives. You will play a crucial role in designing, building, and leading a cross-functional team responsible for enterprise data analytics, data science, data governance, and structured data enablement to support advanced AI/ML use cases. As the Director of Data Analytics, Science & AI Enablement, you will lead the development and deployment of machine learning, generative AI, recommendation systems, and predictive models to enhance product intelligence and automation. You will be responsible for building and scaling AI capabilities across the platform, including personalization, NLP, anomaly detection, and customer segmentation, while ensuring that the models are interpretable, ethical, and aligned with business and customer trust standards. Driving insights into user behavior, product performance, churn prediction, and lifecycle value using customer and usage data will be a critical aspect of your role. You will develop dashboards, KPIs, and self-service analytics tools for marketing, product, sales, and support teams, as well as own the customer analytics roadmap to enhance onboarding, conversion, retention, and upsell opportunities. You will build and lead a high-performance team of data scientists, AI/ML engineers, analysts, and data product managers. Collaborating with various departments such as Product, Engineering, Marketing, Sales, Legal, Risk & Compliance, and Customer Success, you will align data strategy with business objectives and legal requirements. Your ability to communicate findings to senior leadership and influence roadmap decisions using data-backed recommendations will be crucial. Collaborating with Data Engineering, you will ensure scalable data architecture and high-quality data pipelines. Overseeing data quality, governance, and compliance across all analytical and operational systems, you will implement scalable data architecture and governance frameworks, ensuring compliance with data privacy regulations and internal standards. Additionally, you will establish and lead a high-performing global team of data analysts, data scientists, and data engineers. Driving data availability, quality, and governance across the organization to support AI and advanced analytics initiatives will be a key part of your role. You will partner with engineering, product, and business stakeholders to identify opportunities for AI/ML solutions and ensure they are supported by reliable, well-structured data. Serving as a thought leader for data science, analytics, and AI enablement best practices, you will lead the development of dashboards, metrics, and decision-support tools that empower business leaders. To be successful in this role, you should have a Bachelor's or Master's degree in Computer Science, Statistics, Data Science, Engineering, or related discipline. You should possess proven experience working in a SaaS or tech environment with subscription-based metrics, at least 10+ years of experience in data analytics, data science, or related fields, with 3-5 years in a leadership capacity. Strong knowledge of AI/ML concepts, data platforms, and BI tools, as well as deep understanding of data governance, data quality, and metadata management, are essential. Excellent communication and stakeholder management skills, along with prior experience with product instrumentation and event tracking platforms, will be beneficial for this role.,

As a Data Scientist at our company, you will play a crucial role in supporting the development and deployment of machine learning models and analytics solutions that enhance decision-making processes throughout the mortgage lifecycle, spanning from acquisition to servicing. Your responsibilities will involve building predictive models, customer segmentation tools, and automation workflows to drive operational efficiency and improve customer outcomes. Collaborating closely with senior data scientists and cross-functional teams, you will be tasked with translating business requirements into well-defined modeling tasks, with opportunities to leverage natural language processing (NLP), statistical modeling, and experimentation frameworks within a regulated financial setting. You will report to a senior leader in Data Science. Your key responsibilities will include: - Developing and maintaining machine learning models and statistical tools for various use cases such as risk scoring, churn prediction, segmentation, and document classification. - Working collaboratively with Product, Engineering, and Analytics teams to identify data-driven opportunities and support automation initiatives. - Translating business inquiries into modeling tasks, contributing to experimental design, and defining success metrics. - Assisting in the creation and upkeep of data pipelines and model deployment workflows in collaboration with data engineering. - Applying techniques such as supervised learning, clustering, and basic NLP to structured and semi-structured mortgage data. - Supporting model monitoring, performance tracking, and documentation to ensure compliance and audit readiness. - Contributing to internal best practices, engaging in peer reviews, and participating in knowledge-sharing sessions. - Staying updated with advancements in machine learning and analytics pertinent to the mortgage and financial services sector. Qualifications: - Minimum education required: Masters or PhD in engineering, math, statistics, economics, or a related field. - Minimum years of experience required: 2 (or 1 post-PhD), preferably in mortgage, fintech, or financial services. - Required certifications: None Specific skills or abilities needed: - Experience working with structured and semi-structured data; exposure to NLP or document classification is advantageous. - Understanding of the model development lifecycle, encompassing training, validation, and deployment. - Familiarity with data privacy and compliance considerations (e.g., ECOA, CCPA, GDPR) is desirable. - Strong communication skills and the ability to present findings to both technical and non-technical audiences. - Proficiency in Python (e.g., scikit-learn, pandas), SQL, and familiarity with ML frameworks like TensorFlow or PyTorch.,