Data Science / Applied AI Engineer

Overview

About SkyGeni

SkyGeni is a Silicon Valley startup with a global presence, headquartered in San Jose, California, with a growing technology team in India. SkyGeni is the modern way to apply AI to build and progress the right pipeline, optimize performance in real-time and drive sustainable revenue growth.
SkyGeni’s customers include several fast-growing B2B SaaS and cybersecurity companies, all of whom use the platform to spot emerging execution risks and exploit emerging market opportunities. Our mission is to help revenue teams improve predictability, accelerate growth, and optimize Sales and Marketing effectiveness through proactive, AI-driven insights.

Role Overview

We are looking for a Data Science / Applied AI Engineer who enjoys working at the intersection of:

• Business problems
• Data science , AI & Machine Learning
• Real-world product engineering

In this role, you will spend roughly:

• 60% on Data Science / AI / ML / Analytics
• 40% on ETL & backend engineering

You will work closely with founders, product managers, and engineers to turn revenue data into meaningful, cutting-edge product features.

Key Responsibilities
1. Product-Focused Data Science and Applied AI

• Understand customer use cases, business problems, and product requirements.
• Analyze sales, CRM, conversational intelligence, and other revenue related data (pipeline, deals, activities, revenue, conversions etc.).
• Build models and algorithms to:
• Identify risks in sales pipelines.
• Predict deal outcomes and revenue.
• Detect anomalies and performance bottlenecks.
• Identify potential for optimal resource allocation.
• Translate insights into shipping, scalable product features (not just notebooks).

2. Rapid Prototyping & POCs

• Build quick proofs-of-concept for new ideas.
• Experiment with different approaches:
• Statistical models
• ML models
• LLM-based approaches
• Present findings with clear problem statements, assumptions, and outcomes.
• Select the right approach for the problem being solved.

3. Applied ML / AI
Work on:

• Classification / regression problems.
• Time-series forecasting.
• Recommendation and ranking.
• LLM-based reasoning over structured data.
• Balance model quality with real-world constraints:
• Data availability
• Performance
• Explainability

4. Backend & ETL Engineering

• Write production-quality Python code.
• Build and maintain ETL pipelines to:
• Ingest data from CRMs (Salesforce, HubSpot, Microsoft Dynamics 365 etc.)
• Clean, normalize, and transform data.
• Support backend APIs when required.
• Work with SQL / data warehouses.

Required Skills
Core (Must Have)

• Strong in Python
• Strong in TypeScript / JavaScript

Good understanding of:

• Statistics
• Data analysis
• ML fundamentals

Hands-on experience with:

• pandas, numpy, scikit-learn
• Very comfortable with SQL
• Experience working with real-world messy data
• Engineering Mindset
• Ability to design software, write clean, maintainable code.

Understanding of:

• REST APIs
• Backend systems
• Data pipelines
• System design considerations
• Comfortable working with Git, debugging, and production issues.

Nice to Have (Big Plus)
Experience with:

• CRM / sales data
• Time-series data
• Forecasting models

Exposure to:

• LLMs (OpenAI, Claude, etc.)
• Prompt engineering
• MCP servers / RAG systems
• Cloud experience (Azure / AWS / GCP)

What We’re Really Looking For (Unwritten Criteria)
This is what actually matters for an early-stage startup:

• Product thinking: You think in terms of “what problem does this solve?”
• Bias for action: You prefer building over theorizing.
• Comfort with ambiguity: Requirements will be fuzzy.
• Ownership mindset: You treat problems as yours and own solving them.
• Curiosity: You enjoy exploring data and finding patterns.

We are not looking for:

• Pure academic data scientists.
• Kaggle-only profiles.
• People who only want to tune models and not touch production.

Growth Opportunity
At SkyGeni, you will:

• Work directly with customers.
• Interact daily with founders.
• Shape core product features.

Grow into:

1. Lead Data Scientist
2. Applied AI / ML Engineer

The models and features you develop will be used by real world customers and you will actively collaborate with them to solve high impact business problems with AI / ML.