Data Analyst V

YOCHANA IT SOLUTIONS, INC.

  • NY
  • 10 days ago
  • Remote
  • $140,000–$150,000 Per Year

Highlights

Apply advanced statistical modeling, machine learning, and natural language processing (NLP) to analyze large-scale customer support interactions (e.g., chat logs, call transcripts, support tickets) and extract actionable insights. Synthesize and analyze customer feedback from multiple sources (e.g., surveys, digital analytics, behavioral, and operational data) across end-to-end customer journeys to identify trends, pain points, and areas for improvement.

Numbers & Facts

LocationNY (
Remote
)
Salary$140,000–$150,000 Per Year

Description

Role: Data Analyst V

Location: Remote

The pay range for this role is $140000- $150000 per annum including any bonuses or variable pay.

As a Customer Experience Analyst, you will be part of a global practice focused on understanding and learning from our customers. You will leverage customer insights to drive business improvements and promote customer-centric decision-making throughout the organization. You will play a pivotal role in boosting customer satisfaction and loyalty by analyzing feedback, identifying areas for improvement, and collaborating with cross-functional teams to develop and implement strategies that enhance the overall customer experience. By combining your strong background in CX/VoC, expertise in data analysis and storytelling, and passion for customer centricity, you will help shape the future of our customer experience program and contribute to the growth and success of our organization.

Responsibilities:

  • Work with large and complex datasets to solve challenging problems using various analytical and statistical approaches.
  • Synthesize and analyze customer feedback from multiple sources (e.g., surveys, digital analytics, behavioral, and operational data) across end-to-end customer journeys to identify trends, pain points, and areas for improvement.
  • Apply advanced statistical modeling, machine learning, and natural language processing (NLP) to analyze large-scale customer support interactions (e.g., chat logs, call transcripts, support tickets) and extract actionable insights.
  • Use clustering and segmentation techniques to identify common customer issues and recommend targeted solutions or self-service resources.
  • Develop and maintain robust reporting and dashboards to track customer experience metrics and KPIs.
  • Integrate customer feedback data with other transactional, operational, and behavioral data sources to create a comprehensive picture of customer experience drivers.
  • Advocate for customers and influence corrective actions through periodic and ad-hoc reporting, proactively evangelizing insights among key stakeholders.
  • Collaborate with cross-functional teams to identify root causes of customer issues and develop action plans to remediate and measure effectiveness.
  • Communicate complex technical concepts to non-technical stakeholders.

Required Qualifications

  • Degree in Analytics, Statistics, Mathematics, Computer/Data Science, Engineering, or a related field.
  • 6+ years of experience in data analytics, data science, or related fields.
  • 3+ years of experience in customer experience, customer support, or customer insights analytics.
  • Proficiency in crafting compelling stories using complex data sources to provide actionable insights tailored to stakeholders' needs.
  • Experience creating reports, visualizations, and dashboards and communicating results and analyses to technical and non-technical audiences.
  • Experience working with Customer Experience or Voice of Customer metrics (NPS, CSAT, etc.), surveys, and customer feedback.
  • Strong programming skills in Python, R, and SQL.
  • Proficiency in data visualization tools (e.g., Tableau, Power BI).
  • Proficiency in utilizing statistical analytics techniques.
  • Demonstrated ability to work collaboratively with cross-functional teams.

Preferred Qualifications

  • Master's or Ph.D. Degree in a quantitative field.
  • Familiarity with consumer electronics or retail business.
  • Proficiency in conducting predictive analytics or running ML/AL techniques.
  • Experience with machine learning libraries such as Pandas, scikit-learn, TensorFlow, or PyTorch.

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