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Customer Facing Data Scientist Jobs (NOW HIRING)

Plat.AI

Front-Facing Data Analyst

Glendale, CA · On-site

We are seeking a highly analytical and business-savvy Front-Facing Data Analyst to serve as the bridge between our data science team and external stakeholders. This role combines strong technical ...

Highbeam, Inc

Data Scientist

New York, NY · On-site

$200K - $225K/yr

... that guide customer-facing teams * Partner with CS, GTM, and Capital teams to analyze behavior ... data science, financial analysis, or a related quantitative field * Strong background in statistics ...

Tive

Senior Data Scientist

Boston, MA · On-site

$130K - $180K/yr

Own and ship innovative customer-facing data science and AI features across Tive's core roadmap spanning theft detection, routing intelligence, anomaly detection, and alerting. * Drive initiatives ...

Anomali Inc.

Senior Data Scientist

Redwood City, CA · On-site

... customer facing data products, visualizations, and analytics. You will apply advanced machine ... Has presented at a security, data science, or big data conference. * Prior experience working in ...

Compunnel

Senior Data Scientist

Charlotte, NC · On-site

... customer facing product - Strong Python programming skills - Experience/exposure to maintain ... data science experience (background in NLP, then past 4-5 years strong GenAI data science ...

TMS

Data Scientist

New York, NY · On-site

$60 - $62/hr

Data Scientist Duration: 6+ months contract Location: NYC- Hybrid About the Team Joining as a Data ... Customer-facing experience, notably in understanding end user needs and building collaborative ...

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How much do customer facing data scientist jobs pay per year?

As of Jul 24, 2026, the average yearly pay for customer facing data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is a Customer Facing Data Scientist job?

A Customer Facing Data Scientist combines technical data expertise with customer interaction skills to help clients derive value from data-driven solutions. They work closely with customers to understand their business needs, develop models or analyses, and communicate insights effectively. This role often involves pre-sales support, post-sales implementation, and ongoing customer consultation, requiring both strong analytical capabilities and the ability to explain complex data concepts to non-technical audiences.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or data features. Customer facing data scientists often focus on identifying the most impactful variables or actions to optimize models and decision-making processes efficiently.

Is 40 too late for data science?

The Customer Facing Data Scientist role, like many data science positions, values skills and experience over age. Professionals can successfully transition into data science at 40 or later by developing relevant skills such as programming, statistics, and machine learning, often through online courses or certifications. Age is less important than demonstrated ability and continuous learning in this field.

What is data science used for in customer service?

A customer facing data scientist uses data science techniques to analyze customer interactions, identify patterns, and improve service quality. They develop models to personalize experiences, predict customer needs, and optimize support processes using tools like machine learning and data visualization.

What are the key skills and qualifications needed to thrive in the Customer Facing Data Scientist position, and why are they important?

To thrive as a Customer Facing Data Scientist, you need advanced analytical skills, a solid understanding of machine learning, statistical modeling, and a relevant degree—often in Computer Science, Mathematics, or a related field. Experience with programming languages such as Python or R, data visualization tools like Tableau, and knowledge of cloud platforms (e.g., AWS, Azure) or certifications in data science are highly valuable. Excellent communication, active listening, and the ability to translate technical findings into business insights are crucial soft skills. These qualities enable effective collaboration with clients, ensuring data-driven solutions are both technically robust and aligned with real-world business needs.

What does a typical day look like for a Customer Facing Data Scientist?

A typical day for a Customer Facing Data Scientist involves meeting with clients to understand their business objectives, analyzing datasets to extract actionable insights, and preparing clear, impactful presentations or reports for stakeholders. You may collaborate with sales or product teams to translate technical results into business recommendations or proposals. Additionally, there is often a need to provide technical support, answer client questions, and iterate on models based on feedback. The role is highly interactive, blending technical problem-solving with client engagement in a fast-paced, dynamic environment.

What are some customer facing jobs?

Customer facing jobs involve direct interaction with clients or customers, such as sales representatives, customer service agents, account managers, and technical support specialists. These roles often require strong communication skills, problem-solving abilities, and familiarity with customer relationship management tools. They are essential in industries like retail, technology, finance, and healthcare.
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Customer Facing Data Scientist jobs near you
Infographic showing various Customer Facing Data Scientist job openings in the United States as of July 2026, with employment types broken down into 50% Full Time, and 50% Temporary. Highlights an 100% In-person job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Senior Data Engineer, Customer-Facing Data Products

Senior Data Engineer, Customer-Facing Data Products

The New York Times

New York, NY • Hybrid

$116K - $157K/yr

Other

Posted 23 days ago

Job description

About the Role

The New York Times is looking for a Senior Data Engineer to join the Customer-Facing Data Products team to develop real-time data pipelines and APIs that process events and serve aggregated data for customer-facing use cases. You will report to the Engineering Manager for the Customer-Facing Data Products team and build widely reusable solutions to help partner teams solve our most important real-time needs, including behavioral and targeting use cases.

This is a hybrid role based in our New York City headquarters.

Responsibilities:

  • Develop real-time data pipelines using event-driven architectures and streaming technologies.
  • Ingest and organize structured and unstructured data for widespread reuse across patterns.
  • Engineer and scale high availability data serving capabilities to meet customer-facing needs.
  • Implement mechanisms to ensure data quality, observability and governance best practices.
  • Collaborate with software engineers and infrastructure teams to improve pipeline performance and integrate solutions into production environments.
  • Grow the skills of colleagues by providing clear technical feedback through pairing, design, and code review.
  • Stay current with latest technologies, keeping up with the latest advancements in streaming data processing and related technologies.
  • Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

  • 5+ years of full-time data engineering experience shipping real-time solutions with event-driven architectures and stream-processing frameworks
  • Experience with cloud native architectures (AWS preferred), including service offerings and tools
  • Understanding of modern API design principles and technologies, including REST, GraphQL, and gRPC for data serving
  • Programming fluency with Python
  • Experience using version control and CI/CD tools, such as Github Actions and Drone

Preferred Qualifications:

  • Experience developing streaming pipelines with Apache Kafka, Apache Flink, or Spark Streaming
  • Experience building APIs using Python frameworks (FastAPI, Flask)
  • Experience with SQL
  • Understanding of modern data platforms including data lakehouse and medallion architectures
  • Experience collaborating with product and partners to meet shared goals

This role will require limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

#LI-Hybrid

REQ-020049

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