GEP Worldwide
Website:
gep.com
Job details:
Key Responsibilities
1. AI-Powered Campaign Intelligence & Automation
- Campaign Intelligence Engine: Design ML/AI models that
optimize audience segmentation, channel selection, and campaign performance in
real time. - Generative AI for Content: Build and deploy GenAI pipelines
to create, personalize, and optimize marketing content across channels (ads,
emails, landing pages). - Content Intelligence Systems: Develop automated pipelines
that transform long-form content (webinars, whitepapers) into multi-channel
assets using LLMs and NLP techniques.
2. Event & Customer Interaction Intelligence
- Real-Time Data Processing: Build systems that capture and
process event and user interaction data in real time for instant insights and
activation. - AI-Based Enrichment & Sentiment Analysis: Apply NLP
models to enrich lead data, extract intent signals, and analyze customer
sentiment from conversations and engagements. - Predictive Engagement Models: Develop models to predict
attendee behavior, engagement likelihood, and conversion probability.
3. Intelligent Lead Scoring & Revenue Modeling
- Next-Gen Lead Scoring: Move beyond rule-based scoring to
ML-driven, intent-based models leveraging behavioral and firmographic data. - AI Agents for Sales Enablement: Build LLM-powered agents
that research accounts, generate insights, and draft hyper-personalized
outreach for sales teams. - Revenue Forecasting & Attribution Models: Develop
predictive models for pipeline forecasting, multi-touch attribution, and ROI
optimization.
4. Data Platform, MLOps & AI Architecture
- Unified Data Ecosystem: Architect scalable data pipelines
integrating CRM, marketing platforms, web analytics, and third-party data
sources. - MLOps & Deployment: Build and maintain end-to-end ML
pipelines (training, deployment, monitoring) for production-grade AI systems. - Data Quality & Governance: Implement automated data
validation, anomaly detection, and “self-healing” data pipelines. - AI Observability: Monitor model performance, drift, and
impact on business outcomes through automated dashboards.
What You Will Bring
- 3-7 Years of Experience in Data Science, Machine Learning,
or AI Engineering, preferably in B2B, SaaS, or growth-focused environments. - Strong ML & Statistical Foundations: Hands-on experience
in supervised/unsupervised learning, NLP, and predictive modeling. - GenAI Exposure (Must-Have, not optional): Practical
experience working with LLMs (OpenAI, Gemini, Claude), prompt engineering, and
building use cases like content generation, summarization, or chat-based
workflows. - Engineering Capability: Proficiency in Python and SQL, with
experience working on APIs and integrating models into production systems. - Applied AI Mindset: Experience translating business problems
into data science solutions and delivering measurable impact (e.g., lead
scoring, personalization, forecasting). - Working Knowledge of MLOps: Familiarity with model
deployment, monitoring, and tools like Airflow, MLflow, or similar (deep
expertise not mandatory). - Data Handling & Systems Thinking: Experience working
with large datasets, data pipelines, and multiple data sources (CRM, web,
product, etc.). - Stakeholder Collaboration: Ability to communicate insights
and AI use cases effectively with business, marketing, and sales teams.
Click on Apply to know more.