Who are we?
We are a globally expanding software technology company that helps brands communicate more effectively with their audiences. We are looking forward to expand our people capabilities and success in developing high-end solutions beyond existing boundaries and establish our brand as a Global Powerhouse.
We are free to work from wherever we want and go to the office whenever we like!!!
What is the role?
We are seeking an innovative and analytical Senior Data Scientist to join our growing team. The ideal candidate will have a strong background in machine learning, AI, and data analysis. You will work on developing models and algorithms to enhance our RTDM capabilities and drive data-driven decision-making.
Key Responsibilities:
- Develop, implement, and maintain machine learning models and algorithms.
- Work with large datasets to extract insights and drive data-driven decisions.
- Collaborate with data engineers to build scalable data solutions.
- Utilize cloud-based data platforms (e.g., S3, Redshift, Lambda, Kinesis, EMR).
- Conduct exploratory data analysis and feature engineering.
- Choose appropriate algorithms based on the problem type and data characteristics.
- Implement and optimize AI and neural network models.
- Create data visualizations and reports to communicate findings.
- Stay current with the latest research and advancements in data science and AI.
- Mentor and guide junior data scientists and analysts.
Technical Expertise:
- Proficiency in Python and data science libraries (e.g., TensorFlow, scikit-learn, PyTorch).
- Strong experience with noSQL databases (e.g., MongoDB, Cassandra) and big data technologies (e.g., Spark, Hadoop).
- Experience with cloud platforms (e.g., AWS, GCP, Azure).
- Knowledge of data engineering processes and data integration.
- Familiarity with graph databases (e.g., Neo4j) and message queues (e.g., Kafka, SQS).
- Experience with a wide range of ML and AI algorithms:
- Supervised Learning: Linear Regression, Logistic Regression, SVM, Naive Bayes, Decision Trees, Random Forests, Gradient Boosting Machines (GBM), AdaBoost, K-Nearest Neighbors (KNN), Neural Networks.
- Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Anomaly Detection, Autoencoders, Generative Adversarial Networks (GANs).
- Reinforcement Learning: Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, Actor-Critic Methods.
- Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Transformer Models (e.g., BERT, GPT), Capsule Networks.
- Predictive Recommendation Engines: Collaborative Filtering, Content-Based Filtering, Hybrid Systems.
Qualifications:
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
- 5+ years of experience in data science or related roles.
- Understand the business problem and its relevance to business objectives.
- Evaluate model performance using appropriate metrics.
- Strong analytical and problem-solving skills.
- Excellent communication and teamwork skills.
- Entrepreneurial spirit and a passion for continuous learning.
Join our team!
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Last updated on Jul 1, 2024