Data Science
Job Description
We are looking for a Consultant II – Analytics to join our growing team and drive direct marketing performance through data-driven insights. In this role, you will work on end-to-end analytics tasks — from building predictive models and segmentations to uncovering actionable insights and preparing client-ready presentations. This is an exciting opportunity to make an impact on the direct marketing strategies for leading brands in the US.
We Are Looking For Someone Who Has
We Are Looking For Someone Who Has
- Strong hands-on skills in Python/R, MS Excel/ PowerPoint, SAS, and SQL (prior experience in SAS is preferred but not mandatory)
- Solid understanding and experience of machine learning techniques such as linear/logistic regression, decision trees, random forests, XGBoost, and customer segmentation
- Excellent communication skills with the ability to present complex technical concepts to business stakeholders
- Strong attention to detail, self-motivation, and a continuous learning mindset
More Info
Key Skills
random forests
machine learning techniques
R
About Company
Skills:
data engineering , bayesian inference , Machine Learning, Hadoop, Big Data Technologies, Sql, Tensorflow, Nlp, Pytorch, Spark, Statistical Inference, Python, cross-validation strategies, hyperparameter optimization, Scikit-Learn, dimensionality reduction, R
Skills:
Python, Sql, R, Visualization Tools, Machine Learning Libraries
Skills:
SAS, Data Analytics, Python, Machine Learning techniques
Skills:
triton , Cloud Infrastructure, Pytorch, Docker, Python, Apis, Cuda, Git, Linux, Distributed Systems, Kubernetes, Profiling, Continuous batching, GPU utilisation, Tokenisation, Mixed precision, KV caching, Embeddings, Sampling, Evaluation frameworks, Low-level optimisation, Attention, Inference, Decoding, vLLM, Model serving, Transformers, NCCL, Quantisation, Device placement
Skills:
Tensorflow, Pytorch, Docker, Python, AWS, Api Development, Git, Azure, Kubernetes, embeddings, scikit-learn, vector databases, automation workflows, model evaluation, ML lifecycle, Deployment, data preprocessing, LLMs, generative AI, retrieval-augmented generation, Monitoring, prompt engineering, RAG, AI agents