Data Scientist

Bykea is on a mission to enhance the movement of people and parcels across Pakistan, with data science playing a crucial role in making this movement smarter, faster, and more efficient. As a Data Scientist at Bykea, you will tackle challenging problems spanning transport, logistics, commerce, economics, and fintech. Your work will involve applying machine learning, deep learning, forecasting, optimization, simulation, geospatial analysis, and large-scale data processing to develop solutions that directly impact products and operations. This role offers the chance to grow in scope and ownership, working within specialized autonomous data science units focused on distinct business challenges while contributing to company-wide data science capabilities. It is a problem-solving position where you will transform ambiguous business questions into analytical models, build and evaluate solutions, and collaborate across teams to implement impactful results.

Key Responsibilities

- Build predictive models and algorithms: Develop machine learning and statistical models to address challenges such as ETA prediction, marketplace churn, demand forecasting, customer behavior analysis, pricing, incentives, and marketplace optimization.

- Solve complex marketplace problems: Work on intelligent allocation, passenger-driver matching, supply-demand positioning, dynamic pricing, routing, and scheduling to enhance marketplace efficiency through effective algorithms.

- Work with large-scale data: Extract insights and build models using extensive datasets, distributed computing environments, data warehouses, ETL pipelines, and real-time data streams.

- Apply advanced data science techniques: Utilize machine learning, deep learning, geospatial data mining, forecasting, simulation, optimization, NLP, clustering, classification, and other methods to solve real-world problems.

- Collaborate cross-functionally: Partner with Engineering, Product, Marketing, Operations, Finance, and Customer Service teams to define problems, establish metrics, build datasets, run experiments, and translate analysis into business impact.

- Build repeatable data solutions: Create reliable datasets, modeling pipelines, monitoring systems, and analytical processes that enable consistent value extraction from data.

- Experiment and measure impact: Contribute to A/B testing, experiment design, KPI development, business intelligence, marketing effectiveness, ROI analysis, and other measurement frameworks.

- Own problems end-to-end: Manage projects from understanding business context and data preparation through modeling, validation, implementation, monitoring, and iteration, seeking guidance on complex decisions when necessary.

Required Qualifications

- 2 to 5 years of professional experience in Data Science, Machine Learning, Data Engineering, Business Intelligence, Data Architecture, Data Modeling, or related technical roles.

- Strong understanding of machine learning, deep learning, data mining, statistics, and optimization algorithms.

- At least 3 years of hands-on experience with SQL and Python or equivalent tools, demonstrating strong proficiency.

- Experience building and evaluating machine learning models using frameworks such as scikit-learn or Spark MLlib is advantageous.

- Proven ability to manipulate, query, and analyze large datasets, translating findings into actionable business insights.

- Experience implementing and reporting on business KPIs within data warehousing or large-scale data environments.

- Familiarity with distributed data processing or big-data technologies like Spark, Hive, or MapReduce is a plus.

- Strong quantitative and analytical skills with the ability to break down complex problems and identify practical solutions.

- Working knowledge of machine learning techniques including clustering, decision trees, classification, regression, and neural networks, with an understanding of their appropriate applications and trade-offs.

- Ability to work independently, learn quickly, and communicate technical concepts clearly to non-technical stakeholders.

- Strong attention to detail and capability to manage multiple priorities in a fast-paced environment.

- Competence in using AI and automation tools effectively to enhance productivity in analysis, coding, research, documentation, experimentation, and model development, with at least one year of experience.

- Excellent communication skills and a collaborative team-oriented mindset.

Preferred Qualifications and Benefits

- Experience building machine learning models at scale using real-time or streaming data pipelines.

- Familiarity with NoSQL databases, PostGIS, stream processing, or distributed computing platforms.

- Experience working with geospatial data, maps, routing, location intelligence, or marketplace data.

- Knowledge of forecasting, optimization, simulation, recommendation systems, NLP, or experimentation frameworks.

- Proficiency in R or Scala in addition to Python.

- Background in high-volume consumer, marketplace, logistics, mobility, e-commerce, fintech, or similar industries.

- Experience deploying machine learning models from experimentation into production environments.

Additional Information

The data science environment at Bykea involves working with technologies such as Python, SQL, scikit-learn, Spark MLlib, Hive, NoSQL, distributed computing, real-time data pipelines, data warehousing, machine learning, deep learning, and experimentation analytics. The focus is on selecting and applying the right tools and approaches to solve problems effectively rather than mastery of every technology.

Candidates should hold a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Electrical/Computer Engineering, Operations Research, Mathematics, Statistics, or a related quantitative field. Ph.D. candidates or graduates are also welcome. The position is based onsite in Karachi.

Assessment will include a hands-on technical evaluation centered on real data science problems, emphasizing problem-solving approach, data reasoning, model selection, evaluation of trade-offs, and clear communication of solutions.

Success in this role is demonstrated by delivering machine learning and analytical solutions that address significant business challenges, building reliable and scalable models and data pipelines, defining clear datasets and KPIs, collaborating effectively across teams, owning complex problems end-to-end, and continuously improving data science techniques and processes.