Senior AI Engineer/Data Scientist

Job not on LinkedIn

🕒 July 31

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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KANINI

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2003

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

KANINI is a global digital engineering and IT services firm that helps enterprises transform through product engineering, cloud enablement, data analytics & AI, intelligent automation, and ServiceNow solutions. It builds cloud-native applications, modernizes legacy systems, implements AI/ML and data platforms, and delivers managed outcomes via flexible engagement models for clients across healthcare, banking, energy, government and technology sectors.

📋 Description

• Lead end-to-end analysis of large, complex, multi-source datasets to surface patterns driving model inputs • Identify, collect, clean, validate, and transform all data required for prediction model consumption • Design and maintain scalable, production-grade data pipelines (training, validation, inference) • Perform deep EDA, data profiling, and quality audits to ensure model-ready data standards • Architect, train, evaluate, and iterate ML models — supervised, unsupervised, and reinforcement learning • Own feature engineering: selection, extraction, transformation, and dimensionality reduction • Apply advanced techniques: deep learning, NLP, time-series forecasting, ensemble methods • Benchmark, A/B test, and monitor models in production; drive continuous performance improvement • Deploy models via REST APIs (FastAPI/Flask); ensure reproducibility and scalability • Self-direct from problem definition through solution delivery with zero hand-holding • Translate ambiguous business problems into precise, executable data science problem statements • Communicate model results and data insights clearly to technical and non-technical stakeholders • Document all experiments, methodologies, and outcomes — audit-ready and reproducible • Champion best practices across the data science lifecycle; mentor junior team members

🎯 Requirements

• B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or equivalent quantitative field (Master's/Ph.D. strongly preferred) • 5+ years of hands-on data science experience with at least 2 years delivering production-grade ML models • Proven ability to own and deliver end-to-end data science projects independently • Portfolio demonstrating innovation in predictive modeling and measurable business impact • Kaggle rankings, research publications, or open-source ML contributions are a strong plus • Experience in a fast-paced, data-driven, decision-model environment • Statistics (Bayesian inference, hypothesis testing, regression, distributions) • Linear algebra, calculus, and probability applied to ML model design • Supervised & unsupervised learning, anomaly detection, clustering • Time-series analysis & forecasting: ARIMA, Prophet, LSTM • Python (Expert): NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, Plotly • SQL (Advanced): window functions, CTEs, query optimization • Git / GitHub; CI/CD for ML; MLOps with MLflow or Kubeflow • Docker & Kubernetes for model containerization and serving • TensorFlow and/or PyTorch — deep learning architectures • XGBoost, LightGBM, CatBoost — gradient boosting & ensemble methods • Hugging Face Transformers — NLP, LLMs, and fine-tuning • SHAP, LIME — model explainability and interpretability • LLMs / Generative AI / Prompt Engineering — strong advantage • AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML • Apache Spark / PySpark — distributed data processing • Airflow / Prefect — pipeline orchestration

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options