About LatentView Analytics
Founded in 2006, LatentView Analytics began with a shared passion for the world of data. Today, over 20 years on, we've grown into a close-knit community of people united by that same drive — solving real business challenges with data and AI. We work with industry leaders worldwide, specializing in end-to-end analytics that goes beyond the buzzwords to deliver genuine business impact. Our focus has stayed consistent since day one: helping clients derive meaningful insights and drive growth through a thoughtful, sustainable approach to data analytics and AI.
Role Overview
We are hiring Data Scientists to design, build, and operationalize statistical and machine learning solutions across our client engagements. You will work with large-scale and often distributed datasets to uncover patterns, build predictive models, and translate findings into decisions that matter to the business. The specific focus of your role — from applied ML and marketing analytics to platform engineering for GenAI — will be matched to your experience and specialization.
Key Responsibilities
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Model development: Design, build, and evaluate statistical and machine learning models against real business problems, from exploratory analysis through to production-quality solutions
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Data engineering for analysis: Acquire, clean, and process data from large-scale or distributed sources, including feature engineering to strengthen model performance
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Deployment & monitoring: Operationalize models into production pipelines, and monitor performance over time to catch drift, decay, or data-quality issues
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Stakeholder collaboration: Work closely with business, product, engineering, risk, or compliance teams — as relevant to the project — to shape and deliver analytical solutions
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Communication: Present findings, models, and recommendations clearly to both technical and non-technical audiences
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Governance & reliability: Follow responsible AI, data governance, and security practices appropriate to the engagement
Skills & Experience
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4–10 years of relevant experience in Data Science, Machine Learning, or a closely related analytics discipline
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Strong programming skills in Python (or R) for data manipulation, model development, and evaluation
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Proficiency in SQL for querying and processing data, including at scale (e.g. Spark SQL, Hive)
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Hands-on experience with core statistical and machine learning techniques — regression, classification, clustering, or dimensionality reduction — using libraries such as scikit-learn or statsmodels
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Experience working with large-scale or distributed data platforms such as Spark, Databricks, or equivalent cloud environments
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Strong stakeholder management and communication skills — proven ability to translate technical findings into business insights
Good to Have(any one combination)
GenAI & LLM Platform Engineering
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Hands-on GenAI/LLM experience in production, with platforms such as AWS Bedrock, Azure OpenAI, OpenAI, or Anthropic
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REST APIs, RDBMS, and enterprise backend/platform engineering experience
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AI governance, security, guardrails, observability, and cost/token optimization
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Experience leading or mentoring platform engineering teams
Marketing Analytics & Bayesian Modeling
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Bayesian inference and MCMC, with hands-on experience in PyMC or Stan
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Marketing Mix Modeling (MMM), adstock and saturation/Hill transformations
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ROAS, media attribution, and channel decomposition
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Time-series econometrics and constrained optimization for budget allocation
Applied ML Engineering & MLOps
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Experience deploying models with tools such as Docker, Kubernetes, or cloud ML platforms (e.g. GCP Vertex AI, AWS SageMaker)
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Building feature pipelines and workflows that support scalable model training and scoring
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Anomaly detection techniques and model explainability methods (e.g. SHAP, LIME)
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Monitoring frameworks for model performance, data drift, and retraining triggers
Risk, Fraud & Compliance Analytics
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Experience building fraud or financial-crime detection models, including exposure to platforms such as Feedzai
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Domain exposure to FinTech, Payments, or Banking
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Working within regulatory and compliance-driven environments
Distributed Data & Domain Foundations
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PySpark or SparkR, with experience on Databricks, AWS EMR, or equivalent platforms
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Working knowledge of a business domain such as Banking and Financial Services, Retail, Healthcare, or Telco
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Familiarity with data governance concepts and structured analytical workflows
Qualifications
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Bachelor's or Master's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, Engineering, or related) or equivalent practical experience
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Strong analytical, problem-solving, and communication skills
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Exposure to version control (Git) and collaborative development practices
Job Snapshot
Updated Date
12-09-2026
Job ID
J_5934
Location
Bengaluru, Karnataka, India
Experience
3 - 5 Years
Employee Type
Permanent