JIFFY.ai
Location
Trivandrum | India
Job description
Who are we? JIFFY.ai is an AI-powered SaaS "No-code" platform on a mission to enable non-technical innovators to build delightful enterprise software applications in a Jiffy. We are a well funded startup with renowned investors and looking to be the next unicorn. We are democratizing software development and application lifecycle management through a paradigm shift driven by advanced AI and GenAI.
We are a team of passionate, creative problem solvers with a strong drive for results. We work in a transparent, flat and open organizational structure where everyone owns a piece of the puzzle. We believe in the motto of work hard and party harder. Brief Job Description: We are seeking a highly skilled and motivated individual to join our team as a Lead Data Scientist. As a key member of our organization, you will lead a dynamic team of data scientists in leveraging advanced technologies and statistical methodologies to extract actionable insights from diverse datasets. The ideal candidate will have expertise in various domains, including Generative AI, Natural Language Processing (NLP), Computer Vision, Statistical Modelling, Reinforcement Learning, and other critical data science disciplines. Key Responsibilities: Generative AI: o Implement Generative AI technology to autonomously create realistic data,images, or text, expanding the capabilities of traditional rule-based systems. NLP (Natural Language Processing): o Process and analyse human language data to derive meaningful insights,contributing to developing advanced language models. Computer Vision: o Apply advanced computer vision techniques to interpret and understand visual information from images or videos, enhancing our organizations capabilities in visual data analysis. Statistical Modeling: o Apply statistical methods to analyze and model complex datasets, ensuring the accuracy and reliability of insights derived from the data. Reinforcement Learning: o Develop models using libraries such as OpenAI Gym and stable-baselines to enable machines to learn and make decisions, particularly in dynamic and evolving environments. Data Cleaning & Preprocessing: o Exhibit proficiency in cleaning and preprocessing raw data to ensure its suitability for analysis, addressing missing values, inconsistencies, and formatting issues. Problem Definition: o Identify and articulate business questions or problems to be addressed using data-driven approaches, aligning analyses with organizational objectives. Data Acquisition: o Gather relevant data from diverse sources, including internal datasets, public repositories, and through web scraping, ensuring data completeness and accuracy. Exploratory Data Analysis (EDA): o Conduct thorough exploratory data analysis through visualization and statistical summaries to gain initial insights into the dataset. Model Building & Training: o Choose and construct suitable machine learning or statistical models based on identified business problems, ensuring optimal performance and accuracy. Model Evaluation: o Assess the performance of developed models using metrics such as accuracy, precision, recall, and other relevant measures. Model Deployment & Monitoring: o Oversee the deployment of models into production and establish monitoring mechanisms to track and optimize their performance over time. Time Series Analysis: o Demonstrate mastery in tools and techniques for analyzing time-series data, including ARIMA, seasonal decomposition, and Prophet for forecasting purposes. Anomaly Detection: o Implement methods for identifying unusual data points using libraries like PyOD, contributing to the organization's proactive approach to anomaly detection. Data Privacy & Security: o Uphold an understanding of data protection laws (e.g., GDPR, CCPA) and employ techniques for securing data, such as encryption and anonymization, to ensure compliance and safeguard sensitive information. Educational Qualification: Computer Science Engineering or a Post Graduate from a reputed institution Work Experience and Skills required: 5+ Years of hands on experience in Python and Machine Learning Tools & Technologies: Programming Languages Python (Scikit-learn, NumPy, Pandas) Frameworks Fast API,Gradio,Langchain,Flask ,Django IDE Jupyter notebook,Google colab,PyCharm,VSCode Databases SQL (MySQL, PostgreSQL), NoSQL (MongoDB) Visualization Libraries Matplotlib,Seaborn,Plotly,Bokeh,ggplot2 (R) Machine Learning Libraries Scikit-learn, EDA,TensorFlow, Keras, PyTorch, XGBoost, LightGBM, CatBoost, Pandas, NumPy, SciPy, Statsmodels, H2O.ai, MXNet Deep Learning Frameworks TensorFlow, PyTorch, Keras, transformers, MXNet, Deeplearning4j, Microsoft Cognitive Toolkit (CNTK), PaddlePaddle, Apache MXNet, TFLite (TensorFlow Lite), ONNX (Open Neural Network Exchange) Generative AI Models like Stable Diffusion, DALL-E 2, Imagen, GPT- 3,Zora,GPT 4,Gemini NLP (Natural Language Processing),Prompt engineering,NLTK, spaCy, TextBlob, Gensim,Stanford NLP, Transformers (Hugging Face),CoreNLP, PyTorch-Transformers, Flair, Rasa NLU,AllenNLP, FastText Computer Vision OpenCV, TensorFlow, PyTorch, scikit-image, Dlib, Pillow, MXNet, Darknet (YOLO), Keras, MicrosoftCognitive Toolkit (CNTK), Apache MXNet (GluonCV),Hugging Face Transformers Cloud Platforms AWS SageMaker, Google AI Platform, Azure Machine Learning,Amazon Textract,Google Cloud Vision API Model deployment Docker, TensorFlow Serving, Flask, FastAPI, ONNX,AWS SageMaker, Google Cloud AI Platform, Azure,Machine Learning Service, Heroku, MLflow. Accuracy, Precision, Recall, F1 Score, ROC-AUC, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R- squared, Confusion Matrix, Area under the Precision- Recall Curve (AUC-PR), Log Loss, Cohen's Kappa, Matthews Correlation Coefficient (MCC), Sensitivity, Common Evaluation Metrics Specificity, AUC-ROC (Area Under the Receiver Operating Characteristic curve), Cross-Entropy Loss Version Control Git Databases SQL (MySQL, PostgreSQL), NoSQL (MongoDB) What are the benefits and perks of working at JIFFY.ai? We have a hybrid working environment with at least 3 days in the office. One of the best performance-based compensation plans that reward based on impact to the company. Working with the best in class talent. Medical coverage, retirement and leave plans for all family types.Job tags
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