Senior Analyst - Data Science

LatentView Analytics · Remote

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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

<h1>About LatentView Analytics</h1>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.<h1>Role Overview</h1>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.<h1>Key Responsibilities</h1><ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Model development: Design, build, and evaluate statistical and machine learning models against real business problems, from exploratory analysis through to production-quality solutions</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Data engineering for analysis: Acquire, clean, and process data from large-scale or distributed sources, including feature engineering to strengthen model performance</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Deployment & monitoring: Operationalize models into production pipelines, and monitor performance over time to catch drift, decay, or data-quality issues</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Stakeholder collaboration: Work closely with business, product, engineering, risk, or compliance teams — as relevant to the project — to shape and deliver analytical solutions</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Communication: Present findings, models, and recommendations clearly to both technical and non-technical audiences</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Governance & reliability: Follow responsible AI, data governance, and security practices appropriate to the engagement</li></ul><h1>Skills & Experience</h1><ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">4–10 years of relevant experience in Data Science, Machine Learning, or a closely related analytics discipline</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Strong programming skills in Python (or R) for data manipulation, model development, and evaluation</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Proficiency in SQL for querying and processing data, including at scale (e.g. Spark SQL, Hive)</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Hands-on experience with core statistical and machine learning techniques — regression, classification, clustering, or dimensionality reduction — using libraries such as scikit-learn or statsmodels</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Experience working with large-scale or distributed data platforms such as Spark, Databricks, or equivalent cloud environments</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Strong stakeholder management and communication skills — proven ability to translate technical findings into business insights</li></ul><h1>Good to Have(any one combination)</h1>GenAI & LLM Platform Engineering<ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Hands-on GenAI/LLM experience in production, with platforms such as AWS Bedrock, Azure OpenAI, OpenAI, or Anthropic</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">REST APIs, RDBMS, and enterprise backend/platform engineering experience</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">AI governance, security, guardrails, observability, and cost/token optimization</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Experience leading or mentoring platform engineering teams</li></ul>Marketing Analytics & Bayesian Modeling<ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Bayesian inference and MCMC, with hands-on experience in PyMC or Stan</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Marketing Mix Modeling (MMM), adstock and saturation/Hill transformations</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">ROAS, media attribution, and channel decomposition</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Time-series econometrics and constrained optimization for budget allocation</li></ul>Applied ML Engineering & MLOps<ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Experience deploying models with tools such as Docker, Kubernetes, or cloud ML platforms (e.g. GCP Vertex AI, AWS SageMaker)</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Building feature pipelines and workflows that support scalable model training and scoring</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Anomaly detection techniques and model explainability methods (e.g. SHAP, LIME)</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Monitoring frameworks for model performance, data drift, and retraining triggers</li></ul>Risk, Fraud & Compliance Analytics<ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Experience building fraud or financial-crime detection models, including exposure to platforms such as Feedzai</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Domain exposure to FinTech, Payments, or Banking</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Working within regulatory and compliance-driven environments</li></ul>Distributed Data & Domain Foundations<ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">PySpark or SparkR, with experience on Databricks, AWS EMR, or equivalent platforms</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Working knowledge of a business domain such as Banking and Financial Services, Retail, Healthcare, or Telco</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Familiarity with data governance concepts and structured analytical workflows</li></ul><h1>Qualifications</h1><ul><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Bachelor's or Master's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, Engineering, or related) or equivalent practical experience</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Strong analytical, problem-solving, and communication skills</li><li style="font-family:'Times New Roman', serif;color:#000000;background-color:transparent;">Exposure to version control (Git) and collaborative development practices</li></ul>

Collected experience requirement

3 - 5 Years