Axis My India - Data Analyst - Numpy/Pandas

Job Description

About The Company :

Axis My India is Indias foremost Consumer Data Intelligence Company, which in partnership with Google is building a single-stop People Empowerment Platform, the a app, that aims to change peoples awareness, accessibility, and utilization of a slew of services.

At Axis, we are dedicated to making a tangible impact on the lives of millions.

If you're passionate about creating meaningful changes and aren't afraid to get your hands dirty, we want you on our team!

Role Overview

The Data Analyst will support Senior data scientists to manage, analyze, and interpret data for driving business decisions across multiple projects, including the a app and other initiatives.

This position requires advanced statistical knowledge including data science, machine learning techniques and creating AI related tools.

Key Responsibilities

• Conduct relevant statistical tests to validate hypotheses and ensure data-driven outcomes.
• Support Sr data scientist in designing, developing and deploying advanced statistical and machine learning models to analyze complex datasets, including those from the a app and other projects.
• Perform Exploratory Data Analysis (EDA) to identify patterns, anomalies, and opportunities.
• Perform data fusion activities by leveraging techniques to merge, reconcile, and analyze information from disparate systems and formats.
• Identify and employ modern weighing and projection methods to answer key business questions and predict future trends.
• Support data Visualizer with necessary data for real-time data visualization.
• Collaborate with product and research teams by providing feedback based on analytical findings.
• Develop and maintain daily MIS reports and automate highlighting the actionable insights and trends.
• Define and create Key Performance Indicators (KPIs) based on APP data.
• Serve as the gatekeeper for data maps, ensuring data integrity and accessibility for stakeholders.
  
  

Required Skills & Qualifications

• MSc in Statistics or a related quantitative field.
• Master in statistical analysis, predictive modelling and data validation.
• Proficiency in Python is a must with exposure to libraries used for numerical and text analysis such as Pandas, Numpy, PySpark, NLTK, SpaCy, Scikit-Learn, Genism.
• Complete understanding of using both parametric and non-parametric methods.
• Advanced knowledge of survey analytics and EDA techniques.
• Excellent communication skills for cross-functional collaboration.
• Experience with MIS dashboard development and KPI creation.
• High attention to detail and commitment to data quality.
  
  

Preferred Experience (for 1 Year Experience)

• Experience working with app-based data and multi-project analytics environments.
• Familiarity with analyzing multiple survey data.
• Background in creating and managing KPIs aligned with business objectives.
• Exposure to big data technologies.
  
  

Requirements

Technical Skills :

• Data Analysis & Statistical Expertise: Ability to manipulate large datasets, perform data cleaning, and run relevant statistical tests to validate findings and ensure data accuracy
• Advanced Excel Skills: Expertise in Excel formulas, pivot tables, macros, and dashboard creation to complement BI tools and automate reporting tasks.
• Programming Knowledge: Familiarity with Python for data manipulation, exploratory data analysis (EDA), and predictive modelling enhances analytical capabilities.
• Data Validation & Quality Assurance: Skill in validating data integrity and consistency to ensure reliable dashboard outputs.
  
  

Analytical & Business Skills

• Critical Thinking & Problem Solving: Strong analytical mindset to interpret complex data, identify trends, and provide actionable insights.
• Business Acumen: Understanding of the business context to translate data insights into relevant recommendations and feedback for product and research teams.
  
  

Communication & Collaboration

• Effective Communication: Ability to present complex data insights clearly and succinctly to internal stakeholders, including senior executives, through reports, dashboards and presentations.
• Stakeholder Management: Experience collaborating with cross-functional teams, gathering requirements, and incorporating feedback to refine dashboards and reports.
• Coaching & Mentoring: Mentor and guide junior data scientists and analysts, fostering a culture of continuous learning and innovation
  
  

(ref:hirist.tech)