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As a member of this team, you will dive deep into our data and harness your strong data extraction, transformation, and load (ETL) skills to create data structures, build machine learning models, create forecasting , predictive and prescriptive models, build dashboards and visualization to support business strategy and enable GDSP to achieve its goals.
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Our data science track covers most of the below: Topics and content might change based on the market requirements Python NumPy and Pandas Matplotlib data visualization Difference between Machine learning, Artificial Intelligence and Deep learning Data Manipulation: Cleansing-Munging Data Analysis.
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Proficiency with PySpark, Python, SQL, Excel, and data visualization tools (Tableau, Power BI, etc.) Excellent understanding of machine/deep learning techniques and algorithms (XGBoost, random forests, logistic regression, SVM, KNN, k-means clustering, PCA, neural network, deep reinforcement learning, etc.
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Experience with training and deploying NLP deep learning models. Exceptional skills in Python, SQL, and any other programming language for data analysis and visualization. Our full-stack Data Science Team uses Python for research and development.
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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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Deep expertise in AI and data science, with hands-on experience in the latest machine learning techniques. Proficiency in modern analytics tools such as R, Python, SQL, and advanced data visualization platforms.
$143,000 - $240,000 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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The ideal candidate will have a robust background in data science, advanced modeling techniques, deep learning, and Generative AI. Proficiency in Python, SQL, and experience with Nielsen or IRI data is essential.
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You have experience working with large data sets and have either used or are interested in learning dbt and Python. Analyze internal and external data to gain a deep understanding of our Ideal Customer Profile (ICP) and help identify accounts that meet this criteria.
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Expertise in machine learning techniques, classification, clustering, and deep learning. Strong programming skills in Python and experience with libraries like TensorFlow, PyTorch, and Scikit-learn.
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Solid theoretical knowledge of Machine Learning and Statistical concepts, including Deep Learning, as well as performance tradeoffs. Experience working with data-intensive systems and writing production-quality software (preferred Python or golang.
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Proficiency in programming with Python and machine learning development tools/libraries. Familiarity with state-of-the-art machine learning algorithms, especially deep learning techniques including generative AI.
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Experience of Python Programming/ Data Science / Machine Learning experience. Algorithm Implementation: Implement computer vision and deep learning algorithms to support image manipulation, filtering, and transformation, as well as other creative design features.
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Deep understanding of various machine learning algorithms such as regression, classification, clustering, and deep learning. Cloud Technology Utilization: Leverage cloud platforms for ML and data engineering solutions.
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Familiarity with deep learning libraries like Tensorflow, Keras, pytorch. Strong experience in Python data wrangling/analysis using Pandas, Numpy, Matplotlib, seaborn etc. with strong background in designing and building machine learning models / algorithms to implement predictive & prescriptive analytical solutions in areas such as customer loyalty, personalization and recommendations, customer segmentation and profiling, Sales Calls, E-mail and Direct mail targeting, retention analysis, churn assessment etc.
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Knowledge of a variety of deep learning techniques (regression, classification, CNN, RNN, LSTM, etc) - Must. Knowledge of a variety of machine learning techniques (exploratory data analysis, supervised/unsupervised machine learning, regression, random forest, svm, boosting, cross validation, etc) - Must.
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