Drweng1mo ago

Software Developer (Data Analytics)

Montréalmid

EngineeringData ScienceSoftware Developer

0 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

algorithmic thinking, data structures, and complexity-aware design. Practical networking knowledge: HTTP headers/status codes, TCP basics (retransmits, RTT implications), TLS concepts,

Technical Tools

EngineeringData ScienceSoftware Developer

Lead open-ended investigations into HTTP request telemetry to find hidden patterns, anomalies, and regressions.
Spin up reproducible analyses quickly (Python/Jupyter, pandas, numpy) and distill findings into concise notes teams can act on.
Use CS fundamentals and networking knowledge (TCP/IP, HTTP semantics, TLS, CDN/proxy effects) to separate client, network, and server causes.
Build small, robust CLIs/scripts to replay requests, synthesize test traffic, normalize logs, perform lightweight tests, or compute streaming metrics.
Implement pragmatic data-processing approaches for large logs (chunking, Parquet, streaming).
Deliver interactive visualizations and actionable recommendations that our developers can use for triage.

Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Mathematics, or equivalent.
Experience in exploratory data analysis using Python and pandas.
Experience creating data visualizations (Matplotlib, Seaborn, Plotly, etc.); Plotly is a plus.
Strong CS fundamentals: algorithmic thinking, data structures, and complexity-aware design.
Practical networking knowledge: HTTP headers/status codes, TCP basics (retransmits, RTT implications), TLS concepts, and CDN/proxy behaviors and how they appear in telemetry.
Strong communication skills: able to explain technical findings clearly to developers and operations, produce concise write-ups or notebooks, and collaborate effectively across teams.

Nice to Have

~1 min read

Experience with applied statistics and statistical modeling for time-series and anomaly detection (e.g., change-point detection, time-series decomposition, forecasting, or related methods).
Familiarity with pcap/wireshark, distributed tracing, or lower-level network traces.
Past experience in high-pressure investigation environments (operations, incident response).
Background in quantitative roles (quantitative researcher/analyst or data scientist/analyst) or experience working with low-latency data streams is a plus.

Consistent delivery of reproducible investigations that turn ambiguity into prioritized, actionable items.
Dashboards or tools adopted by developers and operations that measurably reduce mean time to detect/triage incidents.
Clear, concise reports that communicate findings and next steps.