Data Scientist

InCommon is hiring on behalf of a fast-growing SF/Bangalore based Voice AI Startup.

About the Role

This is not a typical data role. You wont be building dashboards. You wont be maintaining pipelines no one touches.

You will take messy, noisy, real-world data and turn it into something models can learn from. Think of this as running a gold mine - you take dust and convert it to gold.

We work on speech, language, and real-time systems across 50+ languages.

The difference between a good model and a great one is almost always data quality + data systems. Thats where you come in.

What Youll Work On

Data Pipelines (Real-time + Batch)

• Build high-throughput pipelines for audio, text, and multimodal data
• Streaming + offline processing at scale

Data Quality & Curation

• Cleaning, filtering, deduplication, normalization (numbers, emails, code-mix, etc.)
• Designing heuristics + ML-based data filtering systems

Multilingual Data Systems

• Handling 50+ languages, accents, and code-mixed inputs
• Language-aware normalization and segmentation

Training Data Engine

• Build pipelines that continuously generate better training data from production
• Active learning loops, data selection, sampling strategies

Evaluation & Benchmarking Pipelines

• Create scalable eval datasets across languages and domains
• Automate quality tracking for ASR, TTS, and conversational systems

Data Infra for Research

• Work closely with research team to unblock experiments fast
• Build systems that reduce iteration time from weeks hours

What This Role Is NOT

• Not a dashboard/reporting role
• Not a move data from A to B role
• Not a maintenance-heavy legacy pipeline role

What Were Looking For

• Strong fundamentals in data structures, systems, and pipelines
• Experience with large-scale data processing (audio/text preferred)
• Comfortable with messy, unstructured, real-world data
• Strong coding skills (Python required; systems experience is a plus)
• Understanding of ML/data pipelines (training, eval, data curation)

Bonus (Not Mandatory)

• Experience with speech/audio data (ASR/TTS)
• Familiarity with multilingual datasets
• Experience with streaming systems (Kafka, etc.)
• Exposure to data-centric AI / data quality frameworks