Data Scientist

Yêu cầu ứng viên WHAT WILL HELP YOU SUCCEED?A strong base in statistics along with machine learning is mandatory.Strong experience in machine learning, computer vision, and Gen AI techniques. You should be well-versed in techniques like Object Detection, Face recognition, and Object Tracking, which can help you improve your models. Knowledge of the model life cycle to continuously improve the model after production deployment is essential. Always staying relevant with the latest developments in the field of data science and advocating it to the other Data Scientists within the organization. An expert professional with a lot of zeal to learn and explore new methodologies. You must work in a collaborative environment and come up with innovative ideas to continuously improve the solutions/models. Candidates must be willing to explore and research newer areas/technology/algorithms and look to continuously improve the models.CORE TECHNICAL SKILLSLinear Algebra, Statistics, and strong in PythonExploratory Data Analysis and Data Visualization. This includes using statistical and visualization libraries like numpy, pandas scipy, matplotlib, or software like PowerBI, and Tableau. It is also good to have exposure to statistical analysis packages like SAS and SPSSMust have expertise in Hypothesis testing or A/B Testing. Exposure to multi-arm bandit-based testing will be an added advantage.Supervised and Unsupervised ML using statistical and deep learning techniques. Candidates should be familiar with parametric and non-parametric machine learning methods. They should be able to define a sensible evaluation metric for the machine learning models. Exposure to automated machine learning is an added advantage.Exposure to advanced machine learning algorithms will be an added advantage. This can include reinforcement learning techniques, genetic algorithms, semi-supervised learning methods, etc.Candidates should be able to extract data from multiple data sources. This includes, and is not limited to SQL, NoSQL, and Graph databases. Familiarity with SQL and No-SQL query languages is essential.Data structures and algorithms including space and time complexity requirements.Candidates are expected to have expertise in statistical methods including ANOVA multivariate regression, exploratory and confirmatory factor analysis, multidimensional scaling and cluster analysis.Exposure to model deployment lifecycle. This includes exporting the model, using REST services to expose the model, defining KPI to continuously monitor model performance, model re-training, and transfer learning and basic knowledge of MLOpsExperience on GCP with exposure to Big Query, Vertex AI, Kubeflow, and Generative AI modules.Should keep up to date with the latest trends including Generative AI