Data Scientist (On Site) - Vadodara, Gujarat

We are looking for a Data Scientist with *2–5 years of hands-on experience* to lead and drive data-driven initiatives across Smart Node’s ecosystem. In this role, you will not only build advanced statistical and machine learning models, but also lead end-to-end development, mentor junior engineers, and productionize scalable data products.
You will work closely with cross-functional business units—including Sales, Operations, Supply Chain, and Customer Experience—as well as firmware, cloud, and hardware teams to transform raw IoT telemetry, ERP workflows, and user interactions into scalable, production-grade intelligence.
# *Key Responsibilities*
*Technical Leadership & Team Guidance*
* *Lead Data Projects:* Act as the technical point of contact for data science initiatives, owning solutions from problem formulation to production deployment.
* *Mentorship & Code Quality:* Code-review work, champion modular coding standards, design robust system architectures, and mentor junior data scientists/analysts.
* *Cross-Functional Ownership:* Collaborate directly with business leaders (Sales, HR, Finance, Operations) to translate high-level business goals into technical roadmaps.
# *End-to-End ML & AI Solutions*
* *Demand & Inventory Optimization:* Architecture and end-to-end implementation of time-series predictive demand models to streamline inventory and reduce stockouts.
* *Sales Intelligence:* Build, deploy, and monitor lead scoring and churn prediction models to maximize sales conversion rates and customer lifetime value (LTV).
* *Predictive Maintenance & IoT Telemetry:* Build high-reliability failure forecasting models using streaming IoT device data (e.g., heartbeat logs, MQTT payloads) in collaboration with hardware/cloud teams.
* *NLP & Generative AI Systems:* Design and integrate production-grade LLM applications (e.g., automated support, operational text classification) using modern API/RAG frameworks.
# *Productionization, MLOps & Architecture*
* *Model Deployment:* Deploy ML models into production via scalable microservices (FastAPI/Flask) with real-time monitoring for model drift and performance latency.
* *Pipeline Integration:* Partner with cloud/data engineers to build and maintain robust ETL pipelines integrating mobile app events, cloud infrastructures, and ERP databases.
* *BI & Real-Time Analytics:* Oversee the design of high-throughput real-time dashboards to track hardware health, operational bottlenecks, and core enterprise KPIs.
# *Required Skills*
# *Core Data Science & Engineering*
* *Experience: 2–5 years* of demonstrated experience building, deploying, and maintaining production ML models in a fast-paced environment.
* *Advanced Python:* Deep proficiency in production-level Python (OOP, design patterns, profiling) and core libraries *( Pandas, NumPy, Scikit-learn, XGBoost, LightGBM ).*
* *API Development & Microservices:* Strong experience building and deploying robust REST APIs using *FastAPI, Flask, or Django* using Docker containers.
* *Data Engineering & SQL:* Advanced SQL skills for data modeling, window functions, and handling large-scale unstructured/structured datasets.
# *Machine Learning & AI*
* *Deep Learning Frameworks:* Practical experience using *PyTorch or TensorFlow* for production tasks.
* *NLP & LLM Applications:* Hands-on experience with modern NLP workflows, Hugging Face transformers, and integrating Generative AI APIs / vector databases into production systems.
* *Computer Vision (Practical):* Understanding of vision pipelines (OpenCV, YOLO, ResNet) for edge or cloud image/video analysis.
# *MLOps & Production Tools*
* *MLOps Foundations:* Familiarity with model tracking, registry, and CI/CD tools ( *MLflow, DVC, Git, Docker* ).
# *Good to Have*
* *Edge AI & Embedded Systems:* Hands-on experience with Edge AI deployment *( TensorFlow Lite, ONNX Runtime )* for low-latency IoT or mobile edge execution.
* *IoT Protocols & Streaming:* Experience with IoT communication patterns *( MQTT, WebSockets, Kafka, Kinesis )* and stream processing.
* *Voice Interfaces:* Experience developing or integrating voice AI systems *(Speech-to-Text, Whisper, Alexa/Google Assistant integrations)* .
* *Orchestration:* Experience with pipeline orchestrators like *Airflow, Prefect, or Dagster* .
* *Cloud Infrastructure:* Experience deploying models on cloud environments ( *AWS* e.g., EC2, S3, SageMaker, Lambda OR *GCP / Azure* ).
* *BI Tools:* Hands-on ability to build and guide team output using tools like *Power BI, Tableau, or Apache Superset.*

Work arrangement
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