Senior Data Scientist: Marketing AI & Big Data Solutions

Publicis Groupe; Boston (MA); On-site; USD 89,000 - 136,000; Full time

Benefits offered by this job

Medical coverage

Dental

Vision

Disability insurance

401K

Parental and family leave

Tuition reimbursement

Flexible time off

Job summary

Digitas in Boston seeks a Senior Data Scientist to design analytic solutions for client applications, building predictive models and AI capabilities, and shaping data pipelines across marketing campaigns.

You will translate business questions into analytic plans, work with Python or R, SQL, and distributed systems such as Hadoop or Spark, and communicate results to both technical and non-technical stakeholders. Collaboration with clients and internal teams is essential.

Qualifications

• 3+ years of professional experience in data science or analytics.
• Hands-on experience mining data for decision-focused insights.
• Experience with Python or R, SQL, and distributed computing systems such as Hadoop or AWS.

Responsibilities

• Translate marketing and business questions into analytical plans.
• Ingest, access and integrate disparate big data sources using distributed systems.
• Build and validate inferential and predictive models for client applications.
• Summarize and communicate analytic results to technical and non-technical audiences.
• Collaborate with stakeholders to define objectives and timelines.
• Stay current with advancements in data science and AI tools.

Skills

Data analytics

Python or R

SQL

Distributed computing

Marketing analytics

AI / ML concepts

Communication

Self-motivated

Education

Bachelor's or Master's in a quantitative field

Tools

Hadoop

AWS

Spark

Job description

Digitas in Boston seeks a Senior Data Scientist to design analytic solutions for client applications, building predictive models and AI capabilities, and shaping data pipelines across marketing campaigns.

You will translate business questions into analytic plans, work with Python or R, SQL, and distributed systems such as Hadoop or Spark, and communicate results to both technical and non-technical stakeholders. Collaboration with clients and internal teams is essential.