Data Scientist

BDIPlus•New York, NY

12d•$115,000 - $140,000

About The Position

We are seeking a Data Scientist to join our team. The Data Scientist will be responsible for designing, developing, evaluating, and optimizing machine learning models that solve complex business problems for our enterprise clients. This role collaborates closely with business partners, engineers, and client stakeholders to deliver scalable AI solutions while ensuring models continue to perform effectively in production environments.

Requirements

Passionate about solving business problems through data science and artificial intelligence.
Experienced working with large-scale enterprise data environments.
Comfortable owning machine learning models throughout their lifecycle from development through production.
Strong analytical thinker with excellent problem-solving abilities.
Fast learner with attention to detail.
Outstanding verbal and written communication skills.
Able to collaborate effectively with cross-functional teams and client stakeholders.
Advanced SQL
Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch)
Apache Spark / PySpark
AWS SageMaker (Databricks, Azure ML, or Vertex AI experience is a plus)
Natural Language Processing (NLP), embeddings, entity extraction, and feature engineering
Statistical modeling, regression, experimental design, hypothesis testing, and drift analysis
Automated testing and data validation
Experience deploying and supporting production machine learning models
Git and modern software development practices

Nice To Haves

Databricks, Azure ML, or Vertex AI experience

Responsibilities

Collaborate with business and technical teams to define machine learning objectives, success metrics, and measurable outcomes.
Identify, ingest, transform, and enrich structured and unstructured data for model development.
Engineer features from structured and text-based data, including entity extraction, normalization, embeddings, and feature generation.
Apply statistical and machine learning techniques including classification, regression, clustering, and deep learning models.
Design and execute experiments including A/B testing, hypothesis testing, causal analysis, and model benchmarking.
Build production-ready machine learning models using Python and Spark.
Design automated evaluation methodologies including benchmark datasets, acceptance tests, and promotion gates.
Monitor production model performance, identify model drift and degradation, and continuously improve model accuracy through retraining and optimization.
Develop scalable machine learning solutions that integrate with enterprise AI platforms.
Present methodologies, findings, and recommendations to both technical teams and executive stakeholders.