Assistant Manager - Data Science

LatentView Analytics · Chennai

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Last observed by our collector: 8 October 2026 at 11:39 pm IST. This is not a manual review or a confirmation from the employer.

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Collected role description

Designation : Assistant Manager - Data Science.Level : L3Location : Bengaluru, ChennaiExperience : 7+ years of experience in data engineering/science, machine learning, or related quantitative roles. Job Role : We are seeking a Senior Data Science Engineer – Applied ML to deploy, improve and maintain predictive models that identify customer risk, detect anomalies in product usage, and generate actionable insights for product and customer success teams. A key focus of this role will be building Early Warning Systems (EWS) that identify customers at risk of declining engagement or churn.In this role, you will collaborate closely with product managers, engineers, and other data scientists to transform behavioral data into actionable intelligence that drives product improvements and proactive customer engagement.<div style="color:rgb(19,19,19);line-height:1.6;"> </div>Responsibilities :Machine Learning Pipelines & Operationalization<ul style="color:rgb(19,19,19);"><li style="line-height:1.6;">Maintain and improve scalable ML pipelines using Databricks, Spark, and Python to support model training, scoring, and monitoring.</li><li style="line-height:1.6;">Operationalize existing ML models by integrating them into automated pipelines for batch scoring and reporting.</li><li style="line-height:1.6;">Develop workflows for model retraining, evaluation, and monitoring to ensure models remain reliable as data evolves.</li><li style="line-height:1.6;">Ensure models and pipelines are integrated with production data systems.</li></ul>Anomaly Detection & Data Monitoring<ul style="color:rgb(19,19,19);"><li style="line-height:1.6;">Develop and maintain anomaly detection systems across datasets to identify unusual changes in product usage patterns or data quality issues.</li><li style="line-height:1.6;">Apply statistical and machine learning approaches to detect anomalies in product telemetry and customer engagement signals.</li><li style="line-height:1.6;">Build monitoring frameworks that help identify shifts in data distributions, behavioral trends, or model performance.</li></ul>Early Warning Systems & Predictive Modeling<ul style="color:rgb(19,19,19);"><li style="line-height:1.6;">Implement and scale Early Warning System (EWS) models that identify customers at risk of declining engagement or churn across multiple Adobe products.</li><li style="line-height:1.6;">Replicate and adapt existing predictive models to new products and datasets, ensuring models generalize across different product usage patterns.</li><li style="line-height:1.6;">Evaluate model performance across products and refine features or modeling approaches as needed.</li></ul>Communication & Collaboration<ul style="color:rgb(19,19,19);"><li style="line-height:1.6;">Partner with engineers, and other data scientists to improve customer engagement and adoption strategies.</li><li style="line-height:1.6;">Present findings through visualizations, reports, and technical documentation.</li><li style="line-height:1.6;">Promote best practices in reproducible data science, model validation, and scalable ML workflows.</li></ul> Required skills : <ul style="color:rgb(19,19,19);"><li style="line-height:1.6;">Strong programming skills in Python and SQL, with experience using libraries such as Pandas, NumPy, and Scikit-learn.</li><li style="line-height:1.6;">Experience implementing and operationalizing machine learning models in production environments.</li><li style="line-height:1.6;">Experience working with large-scale data platforms such as Databricks, Spark, or similar distributed data frameworks.</li><li style="line-height:1.6;">Experience operationalizing and maintaining predictive models in B2B settings.</li><li style="line-height:1.6;">Experience implementing anomaly detection techniques for large datasets.</li><li style="line-height:1.6;">Experience applying model explainability methods (e.g., SHAP, LIME, feature attribution).</li><li style="line-height:1.6;">Strong understanding of ML model evaluation techniques.</li><li style="line-height:1.6;">Experience developing feature pipelines and ML workflows that support scalable model training and scoring.</li><li style="line-height:1.6;">Strong communication skills with the ability to translate technical findings into business insights.</li></ul>

Collected experience requirement

7 - 10 Years