Senior Analyst - Data Science

About LatentView Analytics


Role Overview


Key Responsibilities

• Model development: Design, build, and evaluate statistical and machine learning models against real business problems, from exploratory analysis through to production-quality solutions
• Data engineering for analysis: Acquire, clean, and process data from large-scale or distributed sources, including feature engineering to strengthen model performance
• Deployment & monitoring: Operationalize models into production pipelines, and monitor performance over time to catch drift, decay, or data-quality issues
• Stakeholder collaboration: Work closely with business, product, engineering, risk, or compliance teams as relevant to the project to shape and deliver analytical solutions
• Communication: Present findings, models, and recommendations clearly to both technical and non-technical audiences
• Governance & reliability: Follow responsible AI, data governance, and security practices appropriate to the engagement

Skills & Experience

• 410 years of relevant experience in Data Science, Machine Learning, or a closely related analytics discipline
• Strong programming skills in Python (or R) for data manipulation, model development, and evaluation
• Proficiency in SQL for querying and processing data, including at scale (e.g. Spark SQL, Hive)
• Hands-on experience with core statistical and machine learning techniques regression, classification, clustering, or dimensionality reduction using libraries such as scikit-learn or statsmodels
• Experience working with large-scale or distributed data platforms such as Spark, Databricks, or equivalent cloud environments
• Strong stakeholder management and communication skills proven ability to translate technical findings into business insights

Good to Have(any one combination)

• Hands-on GenAI/LLM experience in production, with platforms such as AWS Bedrock, Azure OpenAI, OpenAI, or Anthropic
• REST APIs, RDBMS, and enterprise backend/platform engineering experience
• AI governance, security, guardrails, observability, and cost/token optimization
• Experience leading or mentoring platform engineering teams

• Bayesian inference and MCMC, with hands-on experience in PyMC or Stan
• Marketing Mix Modeling (MMM), adstock and saturation/Hill transformations
• ROAS, media attribution, and channel decomposition
• Time-series econometrics and constrained optimization for budget allocation

• Experience deploying models with tools such as Docker, Kubernetes, or cloud ML platforms (e.g. GCP Vertex AI, AWS SageMaker)
• Building feature pipelines and workflows that support scalable model training and scoring
• Anomaly detection techniques and model explainability methods (e.g. SHAP, LIME)
• Monitoring frameworks for model performance, data drift, and retraining triggers

• Experience building fraud or financial-crime detection models, including exposure to platforms such as Feedzai
• Domain exposure to FinTech, Payments, or Banking
• Working within regulatory and compliance-driven environments

• PySpark or SparkR, with experience on Databricks, AWS EMR, or equivalent platforms
• Working knowledge of a business domain such as Banking and Financial Services, Retail, Healthcare, or Telco
• Familiarity with data governance concepts and structured analytical workflows

Qualifications

• Bachelor's or Master's degree in a quantitative field (Computer Science, Statistics, Applied Mathematics, Engineering, or related) or equivalent practical experience
• Strong analytical, problem-solving, and communication skills
• Exposure to version control (Git) and collaborative development practices

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