Data Scientist AI / ML
I
Inizio Partners CorpAI and analytics
Source API remote eligibility restrictions: United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- A minimum of 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.
- Required Skills
- AWSPythonSQLMachine LearningSparkNLPGenerative AI
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- At least 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics.
- Experience developing and applying machine learning models in areas such as NLP, semantic search, entity resolution, anomaly detection, fraud detection, or graph analytics.
- Experience designing anomaly and fraud detection analyses using classification, clustering, isolation-based techniques, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling.
- Competency with AWS-native data, analytics, AI, and machine learning services.
- Competency with AWS data lakehouse architectures, including Amazon S3-based data lakes, Apache Iceberg or similar open table formats, metadata catalogs, governed access, and scalable query and transformation patterns.
- Experience using Generative AI, large language models, or foundation models for anomaly and fraud detection analysis.
- Experience working with large-scale structured and unstructured data, particularly documents, PDFs, images, extracted fields, and metadata.
- Strong proficiency in Python for data science and machine learning, including Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow, and strong SQL skills.
- Experience building scalable data processing and machine learning pipelines using AWS-native services and frameworks such as Apache Spark.
- Experience integrating models into production through APIs, batch pipelines, event-driven workflows, streaming pipelines, or embedded analytics applications.
- Understanding of model evaluation, validation, monitoring, drift detection, bias assessment, explainability, and performance measurement.
Responsibilities
- Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases.
- Design and develop models and analytical approaches for search, discovery, anomaly detection, fraud detection, and insight generation.
- Develop anomaly and fraud analyses using supervised, unsupervised, semi-supervised, statistical, and graph-based techniques.
- Use Generative AI and foundation models to enrich alerts, summarize cases, synthesize evidence, and support analyst decisions.
- Build natural language processing, semantic search, entity resolution, and relationship analytics capabilities.
- Develop and operationalize data science solutions in an AWS data lakehouse, including data preparation, feature engineering, training, inference, and monitoring.
- Create model evaluation frameworks and approaches for scoring, explainability, traceability, and human review.
- Evaluate models using precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.
- Support dashboards, reporting, alerting, and investigative workflows.
- Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate solutions into production.
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