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# Data Scientist - Core Machine Learning // San, Jose, CA// In- Person Interview Must

- **Company:** AllianceIT Inc
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, BigQuery, Cluster Analysis, Encodings, Continuous Integration, Distributed Computing Environment, Apache Hadoop, Monitoring of Systems, Python (Programming Language), Machine Learning, NumPy, Power BI, Tensorflow, Azure Machine Learning, Software Engineering, SQL Databases, Tableau (Software), Management of Software Versions, Enterprise Software Applications, Feature Engineering, Chatbots, Boosted Trees, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Deep Learning, Model Validation, Pandas, AutoML, Pyspark, Core Data, Scikit Learn, Kubernetes, Model Architecture, Machine Learning Operations, Drift Detection, GPT, Docker
- **Published:** November 14, 2026
- **Apply:** https://www.dice.com/job-detail/7200611d-4dfb-478c-ba55-24f3201567fe

## About the Role

8-12+ years in applied machine learning / data science, * Bachelor's or Master's in CS, Statistics, Mathematics or a related quantitative discipline; PhD a plus.

 * 8+ years applied ML / data science with a verifiable record of models sustained in production.
 * Strong mathematical foundations - linear algebra, probability, statistics, optimization.
 * Demonstrated depth in feature engineering and model architecture design, explainable on a whiteboard for models personally built.
 * Proven end-to-end pipeline ownership, not notebook-stage experimentation.
 * Expert Python and SQL; pandas, NumPy, scikit-learn and PyTorch or TensorFlow.
 * Distributed processing (Spark/PySpark, Hadoop, BigQuery) and a cloud ML platform (SageMaker, Azure ML or Vertex AI).
 * MLOps working knowledge - MLflow, Docker, Kubernetes, CI/CD, model monitoring.
 * Production model observability and drift management - threshold definition, automated alerting, kill-switches and retraining/rollback triggers.
 * Experience designing model routing or fallback logic across multiple models, including the validation applied when a switch occurs.

Preferred

 * Recommendation, ranking or personalization engines at scale; time-series forecasting and anomaly detection on telemetry data.
 * NLP with classical and transformer-based approaches (embeddings, NER, classification, topic modelling).
 * LLM/GenAI exposure as a complement to core ML depth; enterprise technology, networking or telecom domain experience; Tableau or Power BI.

## Description

Data Scientist (Core ML) - hands-on individual contributor, client-facing, Hands-on Data Scientist to design, build and productionize predictive and recommendation models on large-scale enterprise data. This is a core data science role, not a GenAI application development role. GenAI familiarity is welcome, but profiles centred on prompt engineering, RAG assembly or chatbot/agent development without concurrent classical or deep ML model-building ownership are not a fit., * Problem formulation & features - Translate ambiguous business problems into well-posed ML problems; run EDA and statistical validation; design predictive features (aggregations, embeddings, temporal and behavioural signals); own data quality, leakage prevention, sampling and train/validation/test design.

 * Model architecture - Select and architect the right model family (regression/classification, gradient-boosted trees, clustering, time-series and anomaly detection, deep learning, recommendation/ranking); design from first principles rather than AutoML; justify trade-offs on accuracy, latency, interpretability and cost.
 * Experimentation - Hyperparameter optimization, cross-validation, ablation and error analysis; define and defend metrics, baselines and statistical significance for A/B and online tests.
 * End-to-end pipelines - Own ingestion * feature computation * training * deployment * monitoring; automate retraining, versioning and lineage (MLflow or equivalent); deploy scalable model services instrumented for drift, degradation and rollback.
 * Model routing & selection logic - Where multiple models or agents serve a workflow, define routing rules and the validation logic applied when switching models; use semantic/embedding-based mapping of task or agent goals to the appropriate model, with confidence thresholds, fallback order and post-switch output validation.
 * Observability & autonomous correction - Instrument models and pipelines for runtime observability; implement auto-correct and auto-trigger rules (retry, re-route, auto-start of an alternate model or agent, kill-switch) so that failures such as timeouts, low-confidence outputs or degraded responses are handled without human intervention.
 * Drift detection & alerting - Define data and concept drift metrics with explicit thresholds; build automated alerting, kill-switch and escalation paths, and trigger retraining or rollback when thresholds are breached as upstream data changes.
 * Collaboration - Work in agile cross-functional squads; explain model behaviour, limitations and business impact at whiteboard level to technical and non-technical stakeholders; mentor junior data scientists.