Data Scientist in NY Need only locals

Locals to NY only Onsite interview
JOB SUMMARY
Designing and maintaining data
JOB DESCRIPTION
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
Strong experience in machine learning algorithms, predictive modeling, and data mining.
Proficiency in Pyspark, Python pandas (required) for data science workloads.
Strong SQL (required) knowledge and experience with relational databases.
Minimum 3 years of experience with data visualization tools such as Power BI, Dax Queries, and best practices.
Experience with Azure Databricks, Google Cloud, and modern data science libraries (e.g., scikit-learn, pandas, NumPy).
Experience with GenAI and large language models.
Ability to interpret complex datasets and produce actionable insights.
Must know how to analyze the root cause of dashboard errors.
Have experience in ML Ops and have strong coding background.
Have experience with Natural Language Processing (NLP).
Knowledge or experience with A/B Testing.
Working knowledge of designing, training, and implementing machine learning models.
Familiarity with cloud-based infrastructure
Excellent communication and problem-solving skills.
7 or more years of experience in data science and machine learning engineering.
Additional Skills (Skills that are a plus, but not required)

Knowledge of statistical methods and experimental design.
Responsibilities

Key Responsibilities

Advanced Analytics & Machine Learning

Design, develop, and optimize machine learning models (forecasting, classification, clustering).
Apply data mining techniques to uncover patterns and insights in large datasets.
Perform feature engineering, model validation, and performance tuning.
Explore and deploy modern AI and ML approaches to enhance automation and analytics.
Data Preparation & Quality

Prepare structured and unstructured data for modeling and advanced analysis.
Develop scripts and tools for data cleansing, validation, and enrichment.
Collaborate with Data Engineering to maintain efficient data pipelines.
Identify data quality issues and propose remediation.
Analytics, Insights & Reporting

Conduct deep-dive analyses to identify trends and improvement opportunities.
Communicate complex findings in clear, concise ways to technical and non-technical stakeholders.
Support the development of dashboards, metrics, and analytical solutions.
Cross-Team Collaboration

Work with architects, engineers, and analysts to define analytical requirements.
Contribute to conceptual data model design and workflow optimization.
Promote best practices in machine learning, analytics, and data governance.