Senior AI Engineer

Job Description:

We are seeking a highly skilled and experienced Senior Data Scientist to join our dynamic team. The ideal candidate will have a strong background in data science, machine learning, and statistical analysis, with a proven track record of solving complex problems and delivering actionable insights.

Key Responsibilities:

• Lead and manage data science projects from conception to deployment.
• Develop and implement advanced machine learning models and algorithms.
• Analyze large datasets to extract meaningful patterns and insights.
• Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions.
• Communicate findings and recommendations to stakeholders through reports, presentations, and visualizations.

Qualifications:

• Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related field.
• 5+ years of experience in data science or a related role.
• Proficiency in programming languages such as Python, R, or SQL.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong analytical and problem-solving skills.
• Excellent communication and presentation skills.
• Ability to work independently and as part of a team.

Technical Skills Required:

• Advanced knowledge of statistical methods and data analysis techniques.
• Experience with big data technologies (e.g., Hadoop, Spark).
• Proficiency in data manipulation and analysis using libraries such as pandas, NumPy, and SciPy.
• Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud).
• Familiarity with data visualization tools (e.g., Tableau, Power BI).
• Experience with version control systems (e.g., Git).
• Understanding of database management systems (e.g., MySQL, PostgreSQL, MongoDB).
• Knowledge of natural language processing (NLP) techniques and tools.
• Experience with deep learning frameworks (e.g., Keras, Caffe).
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Luigi).
• Proficiency in using distributed computing tools (e.g., Dask, Apache Flink).
• Knowledge of reinforcement learning techniques.
• Experience with time series analysis and forecasting.
• Familiarity with geospatial analysis tools (e.g., GeoPandas, QGIS).

Modules and Skills Needed:

• Data Preprocessing: Data cleaning, normalization, and transformation using pandas and NumPy.
• Exploratory Data Analysis (EDA): Visualization and statistical analysis using matplotlib, seaborn, and plotly.
• Machine Learning: Supervised and unsupervised learning, model evaluation, and hyperparameter tuning using scikit-learn.
• Deep Learning: Neural networks, CNNs, RNNs, and transfer learning using TensorFlow and Keras.
• Natural Language Processing (NLP): Text preprocessing, sentiment analysis, and topic modeling using NLTK, spaCy, and Gensim.
• Big Data Processing: Distributed data processing using Hadoop, Spark, and Hive.
• Cloud Computing: Deployment and management of machine learning models on, Azure.
• Database Management: SQL and NoSQL databases, data warehousing, and ETL processes.
• Version Control: Code management and collaboration using Git and GitHub.
• Data Visualization: Creating interactive dashboards and visualizations using Tableau, Power BI.
• Reinforcement Learning: Implementing RL algorithms and applications using OpenAI Gym and TensorFlow.
• Time Series Analysis: Forecasting and anomaly detection using statsmodels and Prophet.
• Geospatial Analysis: Spatial data analysis and mapping using GeoPandas and QGIS.

Preferred Qualifications:

• Experience with big data technologies (e.g., Hadoop, Spark).
• Knowledge of cloud platforms (e.g., Azure).
• Familiarity with data visualization tools (e.g., Tableau, Power BI).