Data Scientist, Customer Analytics

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Overview of Cresta

Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. The company’s platform combines the best of AI and human intelligence to help contact centers uncover valuable customer insights, automate inefficient processes, and enable team members to work more effectively and efficiently. Founded by leaders from prestigious backgrounds such as Google X and Open AI, Cresta's focus on innovation sets it apart in the rapidly evolving tech industry.

Role Overview

The Data Scientist for Customer Analytics at Cresta is a crucial position within the Customer Success organization. This individual will serve as the technical driver behind measuring the impact on customers—aiming to convert complex customer conversations and interactions into actionable insights that translate into real business value. The candidate will engage in various tasks, from designing experiments to building dashboards, culminating in quantifiable outputs that power discussions regarding return on investment (ROI), renewal negotiations, and more.

Key Responsibilities

The Data Scientist’s role is multi-faceted and includes several critical tasks:

Customer Analytics & Insight Generation

• Conduct exploratory data analysis (EDA) involving conversational, operational, and performance datasets.
• Translate ambiguous business questions into structured analytical frameworks.
• Analyze the impact of changes in workflow, behavior, and product usage.

Experimentation & Pilot Measurement

• Design and analyze A/B tests and quasi-experiments to measure effectiveness and impact.
• Establish key metrics and measurement techniques for pilots and ensure statistically rigorous results.
• Build and maintain reusable templates for experiments to enhance organizational efficiency.

Dashboards, Reporting & Automation

• Develop comprehensive dashboards for internal teams including Customer Success and Sales.
• Create tools using Python and SQL to enhance repeatability and scalability of analyses.
• Standardize reporting packages to improve consistency in internal and customer-facing communication.

Modeling & Advanced Analytics

• Develop complex statistical or machine learning models for tasks such as segmentation and predictive scoring.
• Maintain reusable pipelines to facilitate value insights and roadmap analyses.
• Collaborate with Engineering for insights to be implemented effectively in production environments.

Cross-Functional Collaboration

• Translate findings into narratives that align with business objectives.
• Support Customer Success Managers (CSMs) with data-driven insights relevant for strategic meetings.
• Work closely with Product and Engineering teams to ensure data availability and ongoing analytics enhancement.

Required Qualifications

Candidates targeting this role should meet the following qualifications:

• 1-3 years of relevant work experience in a data-centric position or equivalent academic experience.
• Strong proficiency in SQL and familiarity with managing extensive datasets.
• Proficient in Python, specifically using libraries like Pandas, NumPy, and scikit-learn.
• A solid grounding in statistical concepts and experimental design.
• Ability to construct dashboards in platforms like Hex, Looker, or Tableau.
• Excellent communication skills, especially with non-technical stakeholders, are crucial.

Preferred Qualifications

While not mandatory, the following will give candidates an edge:

• Experience working with conversational data such as call transcripts or chat histories.
• Familiarity with concepts such as causal inference and A/B testing frameworks.
• Background in customer-facing analytics or roles within customer support.
• Exposure to cloud data technologies like Snowflake or Redshift.
• Basic Natural Language Processing (NLP) skills related to customer behavior or intent classification.

Success Criteria

Within the first 6-9 months, a successful candidate should manage to:

• Construct impactful dashboards that become a staple for insights across the Customer Success team.
• Implement consistent methodologies for measuring pilots and experiments.
• Undertake strategic customer analyses that enhance confidence in renewals or expansions.
• Automate processes that significantly reduce manual efforts in analysis.
• Establish themselves as a trusted analytics partner for Customer Success teams.

Compensation

Cresta emphasizes the importance of valuing employee contributions through its compensation strategy. The compensation includes a base salary, potential bonuses, and equity options. Actual salaries will be personalized based on individual experience, skillset, and geographical location while reflecting market standards. The total compensation package is complemented by a comprehensive benefits scheme designed to support employees and their families.

Recruitment Information

In navigating the job application process, candidates should be vigilant against recruitment impersonations impacting various industries. Cresta emphasizes that any legitimate recruiting communications will originate from the @cresta.ai domain, urging applicants to disregard any outreach claiming to be from Cresta through alternative channels. Candidates are also advised to reach out to Cresta through their official recruiting email if uncertain about the legitimacy of any communication.

Conclusion

This position as a Data Scientist in Customer Analytics represents an exceptional opportunity to contribute to an innovative firm dedicated to leveraging AI for enhancing customer interactions. Cresta seeks individuals eager to apply their analytical skills to help redefine customer experiences, making it an ideal role for someone early in their data science career.

This job offer was originally published on jobicy.com