• Work closely with our data science team to analyze and interpret complex datasets, develop predictive models, and contribute to data-driven decision-making.

Desired shift / Working hours: 

Desired Education:

Job Description & Org Chart

1. Ask the right questions to understand the problem.
2. Extract data from multiple data sources.
3. Clean the data and remove all the noise to ensure accuracy and reliability.
4. Integrate and Load Data to a centralized location (if needed).
5. Perform exploratory Data Analysis on the data loaded.
6. Choose the right Predictive or Machine Learning model for the problem.
7. Apply those models and extract meaningful insights.
8. Measure and improve results (if needed).
9. Present final results to a senior and make adjustments based on the feedback.
10. Create and present reports and visualizations to communicate findings to non-technical stakeholders.
11. Stay current with industry trends and advancements in data science.

Desired Skills:

Educational Background: 

a. Bachelor's or master’s degree in data science, Computer Science, Statistics, or a related field.

Technical Skills:

b. Proficient in programming languages such as Python or R.

c. Strong understanding of statistical modeling and machine learning algorithms.

d. Experience with data manipulation libraries (e.g., Pandas, NumPy).

e. Knowledge of data visualization tools (e.g., Tableau).

f. Familiarity with database systems and querying languages (e.g., SQL, Postre SQL).

Analytical Skills:

g. Ability to analyze and interpret complex data sets.

h. Strong problem-solving skills with attention to detail.

Communication Skills:

i. Excellent verbal and written communication skills.

j. Ability to present technical information to non-technical stakeholders.

Team Collaboration:

k. Demonstrated ability to work collaboratively in a team environment.

l. Willingness to learn and adapt to new technologies and methodologies.

Nice to Have (not mandatory):

• Experience with big data technologies (e.g., Hadoop, Spark).

• Knowledge of cloud computing platforms (e.g., AWS, Azure, Google Cloud).

• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).

A willingness to learn and adopt the best practices in statistics, predictive modelling and machine learning to address the client's needs optimally

Tools:

Python, Machine learning frameworks like Spark, TensorFlow or sciket-learn.