Senior Machine Learning Engineer – Scoring, Audience Models

iDelsoft

. Own account and lead scoring end to end, including target definition, label design, feature engineering, model selection, and calibration .

Posted 11/5/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning and data science, with a strong foundation in advanced statistics and model evaluation. Proficient in building and integrating scoring systems, utilizing tools like Elasticsearch and Python, while ensuring model performance and reliability.

Highest-signal resume keywords

Machine Learning ExpertisePython ProgrammingElasticsearch Query DSLModel Evaluation MetricsFeature Engineering

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Hard Skills

Machine LearningDeep LearningAdvanced StatisticsFeature EngineeringModel SelectionPythonScikit-learnXGBoostPyTorchTensorFlow

Soft Skills

CollaborationCommunication

Tools & Technologies

ElasticsearchNode.jsPandasPolarsNumPySciPy

Industry Keywords

B2B DataMartechAdtechCRM EnrichmentFirmographic Data

Tech Stack

Tools & technologies

ElasticSearchJavaScriptNode.jsNumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Own account and lead scoring end to end, including target definition, label design, feature engineering, model selection, and calibration
Build lookalike and similarity systems to identify high-potential accounts and validate them against tuned baselines
Use Elasticsearch as a feature source and serving layer, including aggregations, vector search, and indexed model scores
Design leakage-free evaluation with decision-matched metrics, including lift, precision at k, calibration, and online experiments
Address entity resolution, deduplication, missingness, label noise, sampling bias, and feedback loops
Build training and inference pipelines, feature storage, batch scoring, and near-real-time scoring
Integrate scoring systems with Node.js services
Monitor production models for drift and degradation and retrain on a defensible cadence
Collaborate with product and engineering to surface scores in the application
Explain to customers what scores do and do not mean

Requirements

What you’ll need

4+ years in machine learning or data science roles, including 2+ years shipping supervised models that drove real decisions in production
Formal grounding in machine learning, deep learning, and advanced statistics — a quantitative degree or equivalent rigorous training
Comfort with the mathematics of machine learning, not just APIs
Expertise in estimation and inference, sampling and selection bias, hypothesis testing and power, regularization and bias/variance, probability calibration, and causal versus correlational claims
Strong command of Python, scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow, pandas/Polars, NumPy, and SciPy
Experience extracting and aggregating features from large document stores at scale; readiness to work directly with Elasticsearch query DSL and aggregations
Experience with ranking, propensity, similarity, recommendation, or churn/conversion modeling
Ability to defend model targets, label provenance, validation limitations, and post-launch failures
Experience building ML foundations and infrastructure from an early stage
Engineering discipline with version control, testing, CI, and containers
Ability to hand a scoring service to a Node.js team
Nice to have: Elasticsearch dense_vector and kNN search, significant_terms, rank_feature scoring, index design, and query performance tuning
Nice to have: B2B data, GTM, martech, adtech, CRM enrichment, and firmographic data provider familiarity
Nice to have: approximate nearest neighbour search with pgvector, Qdrant, or FAISS
Nice to have: learning-to-rank, graph/network modeling, MLOps tooling, publications, competition track record, or open-source applied ML contributions