Gen AI and Machine Learning

We are seeking a highly skilled and motivated Data Scientist to join our dynamic team. The ideal candidate will have a strong background in data analysis, deep learning, and GenAI, with the ability to extract meaningful insights from complex datasets and translate them into actionable strategies that drive business growth.

Qualifications:

Education: Master’s in data science, Statistics, Computer Science, or a related field.

Technical Skills:

• Proficiency in programming languages such as Python or R.
• Good understanding of statistical principles for ML applications
• Experience with data manipulation and analysis using libraries such as pandas, NumPy, and SciPy.
• Exceptional knowledge of Deep Learning techniques, model architectures, and parameter fine-tuning
• Very good knowledge of GenAI with good understanding of foundation models and transformer architecture
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure).
• Experience with SQL and relational databases.
• Familiarity with version control systems (e.g., Git).
• Knowledge of big data technologies (e.g., Hadoop, Spark).
• Extensive experience with genomic data is strongly preferred

Soft Skills: Excellent problem-solving abilities, strong communication skills, and the ability to work effectively in a team environment.

Job Responsibilities:

• Data Analysis: Analyze large, complex datasets to identify trends, patterns, and insights that inform strategic business decisions.
• Model Development: Develop, implement, and validate predictive models and machine learning algorithms to solve various business problems.  
• Data Cleaning: Preprocess and clean data to ensure its quality and integrity before analysis.
• Visualization: Create data visualizations and dashboards to communicate findings to stakeholders in a clear and concise manner.
• Collaboration: Work closely with cross-functional teams, including engineering, product management, and marketing, to understand their data needs and provide relevant insights.
• Reporting: Generate comprehensive reports and presentations that summarize key findings and recommendations