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Job Title : Lead Data Scientist ( Machine Learning )
Job Location : Bengaluru
Exp : 6-12 years
Notice Period : 30 days
Key Responsibilities:
Design, build, and optimize scalable batch and streaming data pipelines using distributed framework like Spark/Databricks/Kafka.
Data and ML Engineering thought leadership (What, Why & How) ) - Design & Code - robust data models, feature pipelines, and ETL/ELT frameworks for analytics and ML.
Ensure data quality, observability, lineage, and performance across data platforms.
Build and refine ML models end‑to‑end: feature engineering, training, evaluation, and deployment.
Partner with data scientists to convert prototypes into production‑grade ML solutions.
Implement CI/CD, model versioning, monitoring, and automation across data and ML workflows.
Product Driven Mindset: Collaborate with engineering, product teams to deliver data‑driven outcomes.
Required Skills
7+ years of experience in ML-Data Engineering development.
Strong SQ/NoSQL, Python, PySpark, and ML Models Lifecycle & Frameworks (MlFlow, Spark-ml), Orchestration (Airflow/Oozie/Dagster etc)
Expertise in Big Data modeling, Distributed processing, and Lake & Warehouse architectures at large operational scale.
Hands‑on with ML lifecycle tools (MLflow, Feature Store, model monitoring, Evaluation).
Strong Analytical & Problem Solving Skills - Data/Process Intensive Design/Architecture, Strong debugging, optimization.
Basic hold on foundational modelling concepts & algorithms such as - Regression, Classification and Statistical models.
Good Hold on Concepts- Distributed File Formats, Open table Formats, Distributed transaction management, Workload Parallelizing.
Jands on - Unix, Hadoop, Object store fundamental operations & commands
Basic skilled with containerized processing (Docker + K8s)
Job ID: 149077249
Skills:
Tensorflow, Numpy, Pandas, Pytorch, Python, Statistical Analysis, R, Scikit-learn
Skills:
Nlp, Unsupervised Learning, Sql, Python, Model lifecycle management, supervised learning, AI ML techniques, Risk Scorecard, Optimization, Propensity models
Skills:
data engineering , Distributed Systems, Big Data, Deep Learning, Constraint optimization, graph algorithms, Statistical ML techniques, Signal Processing
Skills:
theano , Sql, Java, Deep Learning, Tensorflow, Pyspark, Machine Learning, Data Science, Shell Scripting, Spark SQL, Python, Nlp, Scala, MLOps Tools, GraphDBs, Data Mining Techniques, NoSQL Databases, Go, Time Series Forecasting, Caffe, Torch, Recommendation Engines
Skills:
Tensorflow, Nlp, Pytorch, Docker, Python, Azure ML, Kubernetes, vector search, SAM, Pinecone, CI CD, Whisper, cloud ML stacks, Vertex AI, video transformers, LLaVA, LangChain, multimodal LLM frameworks, ViT, speech models, BLIP, SageMaker, Cv, FAISS, Weaviate, LlamaIndex
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