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• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• W2-Permanent

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• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 27th Jan 2024

Statistical Tools: This implies proficiency in statistical analysis. It could involve using tools such as R or Python libraries like NumPy and Pandas for statistical computations.

Innova: It seems like you're referring to a specific tool or technology. If "Innova" is a software or framework used in data analytics or architecture, the candidate would need expertise in it.

P Test: This suggests familiarity with statistical hypothesis testing, particularly using the p-test. Understanding how to design experiments, collect data, and analyze results using statistical tests is crucial.

Hypothesis Testing: This is a broader concept and could involve various statistical tests like t-tests, chi-square tests, ANOVA, etc. The candidate should be adept at formulating and testing hypotheses based on data.

Python: Python is a versatile programming language commonly used in data analysis, machine learning, and big data processing. Proficiency in Python, including relevant libraries like NumPy, Pandas, and scikit-learn, is important.

Big Data: This indicates experience with handling and analyzing large datasets. Knowledge of big data technologies such as Hadoop, Spark, or distributed computing frameworks is likely relevant.

Responsibilities may include:

Designing and implementing data architectures for efficient storage and retrieval.
Conducting statistical analyses to derive insights from data.
Performing hypothesis testing to validate or invalidate assumptions.
Utilizing Python for data manipulation, analysis, and visualization.
Working with big data technologies to handle large datasets.
Collaborating with cross-functional teams to understand business requirements and providing data-driven insights.
Qualifications:

Strong background in statistics, data analysis, and data architecture.
Proficiency in using statistical tools, Innova, and performing hypothesis testing.
Hands-on experience with Python for data analysis and manipulation.
Knowledge of big data technologies and distributed computing.
Strong problem-solving skills and the ability to communicate findings effectively.