Data Scientist

Position : Data Scientist

Report to : Head, Business Analytics

Overview of the Position:

This role will utilize advanced analytical techniques, statistical methods, and machine learning

algorithms to extract meaningful insights from complex datasets.

The role required to execute the below tasks and initiative:

Data Mining: Identify and extract valuable data from various sources such as databases, APIs,

and web scraping.

Feature Selection and Classification: Utilize machine learning algorithms to choose the most

relevant features from the dataset, develop classifiers, and refine them for better accuracy.

Data Preprocessing: Clean and format both structured (like SQL databases) and unstructured

data (like text or images) to prepare it for analysis.

Data Integrity: Implement processes to ensure data quality by validating and cleansing data

sets to eliminate inconsistencies and errors.

Pattern Analysis: Employ statistical methods and machine learning techniques to analyze large

datasets, uncover trends, and derive actionable insights.

Predictive Modeling: Design and develop algorithms that predict outcomes based on

historical data, helping to inform strategic business decisions.

Solution Proposals: Suggest data-driven strategies to address specific business problems,

leveraging analytical insights to improve decision-making.

Collaboration: Work closely with cross-functional teams, including Business and IT, to align

data initiatives with organizational goals and ensure technical feasibility.

To be successful in this role, we are expecting the below:

Educational Background: A degree in Computer Science, Engineering, or a related field.

Proven Experience: Demonstrated Min 2 years experience in Data Scientist role, showcasing

the ability to apply these skills in real-world scenarios.

Programming Skills: Proficiency in languages like R and Python for statistical analysis and

machine learning, alongside SQL for querying databases.

Statistics: Strong foundation in applied statistics, understanding key concepts such as

statistical tests, probability distributions, regression analysis, and maximum likelihood

estimation.

Machine Learning: Familiarity with various machine learning algorithms, and enabling the

selection of appropriate models for specific tasks.

Data Wrangling: Ability to preprocess and clean datasets, addressing issues like missing

values, inconsistencies, and outliers to ensure data quality.

Hands-on Experience with Data Science Tools: Practical experience using various data

science tools and libraries, such as scikit-learn, TensorFlow, or similar platforms, to

implement machine learning and analysis tasks.

Analytical Mind and Business Sense: Ability to think critically about data in a business

context, linking insights to strategic objectives and operational decisions.

Experience with big data technologies (e.g., Hadoop, Spark) is a plus.

Familiarity with cloud platforms like AWS, Azure, or Google Cloud.