Data Science Analyst

Data Science Analyst

The Spec Analytics Analyst is a trainee professional role. Requires a good knowledge of the range of processes, procedures and systems to be used in carrying out assigned tasks and a basic understanding of the underlying concepts and principles upon which the job is based. Good understanding of how the team interacts with others in accomplishing the objectives of the area. Makes evaluative judgements based on the analysis of factual information. They are expected to resolve problems by identifying and selecting solutions through the application of acquired technical experience and will be guided by precedents. Must be able to exchange information in a concise way as well as be sensitive to audience diversity. Limited but direct impact on the business through the quality of the tasks/services provided. Impact of the job holder is restricted to own job.

Key Responsibilities

• Data Collection & Cleaning: Assist in collecting, cleaning, and preprocessing large datasets to ensure data quality and integrity.
• Exploratory Data Analysis (EDA): Conduct basic exploratory data analysis to identify trends, patterns, and insights.
• Feature Engineering Support: Support the creation and selection of features for machine learning models.
• Model Development Assistance: Help in building, training, and evaluating basic machine learning models under guidance.
• Documentation: Maintain clear and concise documentation of data processes, models, and analytical findings.
• Collaboration: Work closely with senior data scientists, engineers, and business stakeholders.
• Learning & Development: Actively participate in training programs, workshops, and self-study to enhance data science skills.
• Reporting: Assist in preparing reports and visualizations to communicate findings effectively.
• Development of search techniques / esp semantic search
• Development of solutions using RAG framework for building Gen AI solutions
• Development of Agentic solution using frameworks like Langchain / Google Agent space, etc

Qualifications

Education

• Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, Engineering, or a related discipline.

Essential Skills (Technical)

• Programming: Foundational knowledge in Python (Pandas, NumPy, Scikit-learn) or R.
• Database Skills: Basic understanding of SQL for data extraction and manipulation.
• Statistical Concepts: Solid understanding of basic statistical concepts (e.g., hypothesis testing, regression).
• Data Visualization: Familiarity with data visualization tools/libraries (e.g., Matplotlib, Seaborn, Tableau, Power BI).
• Machine Learning Basics: Conceptual understanding of common machine learning algorithms (e.g., Linear Regression, Logistic Regression, Decision Trees).

Essential Skills (Soft)

• Problem-Solving: Strong analytical and problem-solving abilities.
• Communication: Excellent verbal and written communication skills to explain technical concepts to non-technical audiences.
• Learning Agility: High curiosity and a strong desire to learn new technologies and methodologies.
• Teamwork: Ability to work effectively in a collaborative team environment.
• Attention to Detail: Meticulous approach to data handling and analysis.

Preferred Qualifications (Optional But Beneficial)

• Experience with version control systems (e.g., Git).
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP).
• Experience with big data technologies (e.g., Spark, Hadoop).
• Participation in data science bootcamps, online courses, or personal projects/Kaggle competitions.

What We Offer

• Structured mentorship program with experienced data scientists.
• Opportunities for continuous learning and professional development.
• Exposure to real-world data science challenges and diverse datasets.
• A collaborative and innovative work environment.
• Competitive salary and benefits package.
• Pathway to a full-fledged Data Scientist role upon successful completion of the trainee program.

Key skills/competency

• Data Collection
• Data Cleaning
• Exploratory Data Analysis
• Python
• SQL
• Statistical Concepts
• Machine Learning
• Data Visualization
• Problem-Solving
• Communication