Data Scientist - 4+ Year Experience

Location: Karachi, Pakistan

Position Summary:

We are seeking an experienced Data Scientist with 4+ years of hands-on experience to join our growing data team. The ideal candidate will be responsible for analyzing large datasets, developing predictive models, and providing actionable insights to support business decision-making. You will use advanced statistical, machine learning, and data mining techniques to solve complex problems, and collaborate closely with stakeholders from various teams to turn data into meaningful and impactful insights.

As a Data Scientist, you will work in a fast-paced environment, leveraging data-driven solutions to optimize business processes, enhance customer experiences, and improve operational efficiency.

Key Responsibilities:

Data Analysis & Exploration:

Analyze large, complex datasets to identify trends, patterns, and insights that can be used to improve business operations and decision-making.

Clean, preprocess, and transform data into structured formats suitable for analysis using tools like Pandas, NumPy, and SQL.

Explore data through visualizations, descriptive statistics, and exploratory analysis to uncover potential opportunities for business growth.

Machine Learning & Predictive Modeling:

Develop and deploy machine learning models to solve business problems such as customer segmentation, recommendation engines, predictive analytics, and classification tasks.

Work with supervised and unsupervised learning techniques (e.g., Linear/Logistic Regression, Random Forest, XGBoost, K-Means Clustering, KNN).

Apply deep learning techniques (e.g., Neural Networks, CNNs, RNNs) to solve advanced problems like image processing or time series forecasting.

Data Wrangling & Feature Engineering:

Handle missing data, perform feature selection, and engineer features to improve model performance.

Work with time-series data, text data (e.g., NLP tasks), and other types of unstructured data to extract meaningful features.

Statistical Analysis & Experimentation:

Conduct statistical analyses (e.g., hypothesis testing, significance testing) to validate the impact of changes in business processes or models.

Design and analyze A/B tests and experiments to measure the effectiveness of various strategies and tactics.

Data Visualization & Reporting:

Use visualization tools like Matplotlib, Seaborn, Plotly, or Tableau to create meaningful reports and dashboards that communicate key insights to stakeholders.

Develop interactive data visualizations and dashboards to monitor business KPIs and model performance.

Present technical findings in a clear and concise manner, tailored to both technical and non-technical audiences.

Collaboration & Cross-Functional Teamwork:

Work closely with product managers, engineers, and business teams to understand requirements and ensure data-driven solutions align with business goals.

Collaborate on the design and implementation of new data pipelines and machine learning workflows.

Provide mentorship and guidance to junior data scientists or analysts.

Model Deployment & Monitoring:

Deploy machine learning models into production environments, working closely with DevOps and software engineering teams to ensure model integration.

Continuously monitor the performance of deployed models and recommend improvements or retraining when necessary.

Continuous Learning & Innovation:

Stay updated with the latest advancements in machine learning, data science, and artificial intelligence (AI).

Evaluate and experiment with new algorithms, tools, and techniques to improve the effectiveness of data science initiatives.

Required Qualifications:

Experience: At least 4 years of hands-on experience in Data Science, Machine Learning, or AI, preferably in a business or product-driven environment.

Education: A degree in Computer Science, Data Science, Mathematics, Statistics, or related field. A Master’s or PhD is a plus.

Technical Skills:

Programming: Proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, Keras, or PyTorch.

Machine Learning: Experience with building and deploying machine learning models, including supervised, unsupervised, and reinforcement learning techniques.

Data Wrangling: Expertise in handling large, messy datasets and transforming them into a usable format for analysis.

SQL: Strong SQL skills for data extraction and manipulation from relational databases (MySQL, PostgreSQL, MS SQL Server, etc.).

Statistical Analysis: Understanding of statistical techniques, hypothesis testing, and data analysis methods.

Big Data: Familiarity with big data technologies such as Hadoop, Spark, or Dask is a plus.

Cloud Platforms: Experience with cloud platforms like AWS, Azure, or Google Cloud for machine learning model deployment and data processing.

Data Visualization: Experience with visualization libraries (e.g., Matplotlib, Seaborn, Plotly) or BI tools like Tableau or Power BI.

Desired Skills & Attributes:

Problem Solving: Strong analytical and problem-solving skills, with the ability to tackle complex business challenges using data-driven insights.

Communication: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Collaboration: Ability to work effectively in cross-functional teams, collaborating with engineering, product, and business teams.

Curiosity & Creativity: Passionate about finding innovative solutions and continuously improving processes through data-driven insights.

Adaptability: Ability to quickly adapt to new technologies, business requirements, and work in a fast-paced, changing environment.

Leadership: Willingness to mentor and guide junior data scientists and analysts, fostering a learning culture within the team.

Certifications (Preferred but not required):

Certified Data Scientist (e.g., from Microsoft, IBM, Google).

AWS Certified Machine Learning – Specialty.

Google Cloud Professional Data Engineer.

Benefits:

Competitive salary based on experience.

Health insurance and other standard company benefits.

Professional development opportunities, including certifications and training.

Flexible working hours and a dynamic work environment.

Opportunities for career growth and advancement.