About inPowered: inPowered’s AI platform enables brands to maximize their content marketing ROI. Powered by Artificial Intelligence and years of machine learning, their Content Intelligence and Content Distribution solutions allow marketers to collect proprietary data and use it to drive real ROI – positively changing brand perception, increasing action taken onsite, lead generation and user engagement. inPowered was founded in 2014 by Peyman and Pirouz Nilforoush after selling their previous company, NetShelter, to Ziff Davis. Description: We are seeking a brilliant data scientist to help accelerate the build-out of our client-facing content marketing platform. The candidate will be working closely with the engineering and AI teams to apply statistical modeling and natural language processing techniques to analyze large-scale text data and generate insights for various business domains. You, together with the AI team, will be working to design, implement, and evaluate data-driven solutions that can enhance customer experience, optimize business processes, and support decision-making. The ideal candidate is passionate and hungry to grow, with significant experience in AI content generation & AI optimization. Capable of providing extraordinary output in terms of quantity and quality. Responsibilities: Perform data preprocessing, exploration, and analysis on text data from various sources Develop and apply statistical models and NLP methods such as topic modeling, sentiment analysis, text classification, text summarization, etc. to extract relevant information and patterns from text data Communicate and visualize the results and insights of the data analysis in a clear and concise manner Collaborate with cross-functional teams to understand the business needs and objectives, and provide data-driven recommendations and solutions Requirements: Solid knowledge of foundational AI, ML, and data science concepts Proficient with Python programming and working with ML frameworks (NumPy, pandas, PyTorch, TensorFlow, sci-kit-learn, spacy) Hands-on experience with deploying models to production and building ML pipelines (Docker, CI/CD/CT) Experience with Time Series and NLP problems Solid Knowledge of Supervised and Unsupervised Machine Learning Models Knowledge of Feature Engineering, Data Wrangling/Cleaning Knowledge of Jupyter notebooks and big data environments/training (Spark, Hadoop, etc) Knowledge of statistical analysis, hypothesis testing, and design of experiments is a plus (changed to required if looking for AI optimization professional) Experienced in AWS/Azure Cloud Platforms focused on Data Tools is a plus Experienced in Data Visualization Tools, MLOps, and MLCanvas. Explainable/Interpretable AI is a plus Strong understanding of software engineering best practices. Good verbal, interpersonal, and written communication English skills Operate well within fast-paced, small teams 5+ years working with machine learning algorithms, deep learning frameworks, and common data science libraries Powered by JazzHR CijfPEljDx #J-18808-Ljbffr
scikit-learn feature-engineering statistics Artificial intelligence (AI) Amazon Web Services (AWS) Apache Spark spacy Azure MLOps Docker NumPy Machine Learning time-series Hadoop Natural language processing (NLP) CI/CD jupyter-notebook pandas Data Science Python data-wrangling data-visualization Big data TensorFlow hypothesis-test PyTorch