Location :- 4 Irving Place, NY (Hybrid)
Duration :-12+ Months
Interview Process:- 1st video, 2nd Face 2 Face.
Need local candidates only.
Job Description:-
Required Qualifications:
• 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
o Design, develop, and optimize machine learning models (forecasting, classification, clustering).
o Apply data mining techniques to uncover patterns and insights in large datasets.
o Perform feature engineering, model validation, and performance tuning.
o Explore and deploy modern AI and ML approaches to enhance automation and analytics.
Data Preparation & Quality
o Prepare structured and unstructured data for modeling and advanced analysis.
o Develop scripts and tools for data cleansing, validation, and enrichment.
o Collaborate with Data Engineering to maintain efficient data pipelines.
o Identify data quality issues and propose remediation.
Analytics, Insights & Reporting
o Conduct deep-dive analyses to identify trends and improvement opportunities.
o Communicate complex findings in clear, concise ways to technical and non-technical stakeholders.
o Support the development of dashboards, metrics, and analytical solutions.
Cross-Team Collaboration
o Work with architects, engineers, and analysts to define analytical requirements.
o Contribute to conceptual data model design and workflow optimization.
o Promote best practices in machine learning, analytics, and data governance.
Thanks & Regards
Piyush Singh | Technical Recruiter
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