Job Description:
We are seeking a talented and experienced Data Scientist with a focus on artificial intelligence and machine learning to join our team. The ideal candidate will have a strong background in data science, statistics, and programming, with expertise in developing and implementing AI/ML algorithms and models. As a Data Scientist, you will play a crucial role in analyzing data, building predictive models, and generating insights that drive decision-making and innovation.
Responsibilities:
- Collect, preprocess, and analyze large datasets using statistical and machine learning techniques to uncover insights, patterns, and trends.
- Design, develop, and implement AI/ML algorithms and models, including supervised and unsupervised learning, deep learning, and reinforcement learning methods.
- Evaluate and optimize AI/ML models for performance, accuracy, and scalability, using techniques such as hyperparameter tuning, feature selection, and model ensembling.
- Collaborate with cross-functional teams to define business problems, develop predictive analytics solutions, and deploy AI-powered applications and services.
- Interpret and communicate findings, recommendations, and insights to stakeholders through data visualizations, reports, and presentations.
- Stay updated on the latest advancements in AI/ML research, methodologies, and tools, and apply them to solve real-world problems and drive innovation.
- Mentor and guide junior data scientists, provide technical expertise and support, and foster a culture of continuous learning and development.
- Bachelor's degree in Computer Science, Statistics, Mathematics, or related field. Advanced degree (Master's or Ph.D.) preferred.
- Experience in data science, with a focus on AI/ML development and implementation.
- Proficiency in programming languages such as Python, R, or Scala, and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn).
- Strong understanding of statistical analysis, machine learning algorithms, and deep learning frameworks (e.g., TensorFlow, PyTorch).
- Experience with data preprocessing, feature engineering, and model evaluation techniques, as well as knowledge of data visualization tools and techniques.
- Excellent problem-solving skills, analytical thinking, and attention to detail.
- Strong communication and collaboration abilities, with the ability to work effectively in multidisciplinary teams and communicate complex technical concepts to non-technical stakeholders.
- AWS Certified Machine Learning Specialty
- Microsoft Certified: Azure AI Engineer Associate
- Google Cloud Professional Data Engineer
- IBM Data Science Professional Certificate
- TensorFlow Developer Certificate
- Domain Knowledge: Expertise in specific domains such as healthcare, finance, e-commerce, or manufacturing can provide valuable context and insights for analyzing data and developing AI/ML solutions tailored to industry-specific challenges and opportunities.
- Natural Language Processing (NLP): Proficiency in NLP techniques and libraries such as NLTK, SpaCy, or transformers can enable the analysis, interpretation, and generation of text-based data, including sentiment analysis, named entity recognition, and text summarization.
- Computer Vision: Familiarity with computer vision algorithms and frameworks such as OpenCV, TensorFlow Object Detection API, or PyTorch Vision can facilitate the analysis, interpretation, and manipulation of visual data, including image classification, object detection, and image segmentation.
- Time Series Analysis: Knowledge of time series analysis methods and techniques, including ARIMA, LSTM, or Prophet, can be beneficial for analyzing temporal data and forecasting future trends and patterns in time series datasets.
- Reinforcement Learning: Understanding of reinforcement learning algorithms and frameworks such as OpenAI Gym, RLlib, or Stable Baselines can enable the development of AI agents that learn optimal decision-making strategies through interaction with an environment.
- Bayesian Statistics: Familiarity with Bayesian statistics principles and methodologies, including probabilistic modeling, Bayesian inference, and Bayesian optimization, can facilitate uncertainty quantification, decision-making under uncertainty, and model calibration.
- Data Engineering: Proficiency in data engineering skills such as data wrangling, data preprocessing, data integration, and data pipeline development using tools and technologies such as Apache Spark, Apache Kafka, or Airflow can streamline the data preparation process and support scalable AI/ML workflows.
- Model Deployment and Productionization: Experience with model deployment and productionization techniques, including containerization, microservices architecture, and model serving platforms such as TensorFlow Serving or Seldon Core, can facilitate the deployment of AI/ML models into production environments for real-time inference.
- Ethical AI: Awareness of ethical considerations and societal implications related to AI/ML, such as bias mitigation, fairness, interpretability, and privacy-preserving techniques, is important for developing responsible AI solutions that uphold ethical standards and promote trust and transparency.
- Soft Skills: Strong interpersonal skills, teamwork, communication, and stakeholder management abilities are essential for effectively collaborating with cross-functional teams, understanding business requirements, and conveying insights and recommendations to diverse audiences.
