Data Scientist (analysis/experimentation) building decision-support models and clear, visual analytics for real-world problems.

• Strengths: Experiment Design, Feature Engineering, Robust Validation, Model Building, and Stakeholder Translation.
• Domains: Finance, Healthcare, Business, Retail/Commercial Analytics, and Sports Analytics
• Main Languages: Python, R, SQL, Typescript, and React

• Email: palashcscs@outlook.com
• LinkedIn: in/pgds
• GitHub: PGupta-Git

• Start with a decision and a measurable target (KPI definition comes first).
• Establish baselines, define leakage-safe splits, and validate with time-aware backtesting when appropriate.
• Prefer simple, explainable models when they perform; add complexity only when it wins on validated metrics.
• Communicate uncertainty (calibration, intervals, sensitivity checks) and translate results into concrete actions.

• Published Article: A pragmatic, parallel-arm, randomised trial on the effects of two repeated-sprint training protocols on fitness outcomes in semi-professional male soccer players: preliminary report (Science and Medicine in Football, 2026) | Data & Code Repository
• ORCID: https://orcid.org/0009-0000-0172-4009

Active contributor to popular open-source projects, libraries, and desktop utilities:

• brilliantnz/flickernaut (GNOME Shell Extension adding custom Nautilus context menu entries)
  • Invalid Apps & Desktop Files Fix (PR #9): Resolved a critical bug causing extension crashes by implementing robust try-except error handling for invalid or missing desktop files (handling null Gio.DesktopAppInfo returns).
  • Duplicate Preferences Entries Fix (PR #9): Prevented application entries from appearing twice in the preferences dialog by ensuring the chooser respects the NoDisplay=true desktop configuration.
• andrewRowlinson/mplsoccer (Matplotlib-based soccer visualization library)
  • Speedometer & Gauge Charts (PR #118): Designed and implemented the core Speedometer class, enabling highly customizable gauge and speedometer charts (implements Issue #16).
  • Curved Radar Text Labels (PR #120): Authored curved label support for radar charts along circular arcs using vector glyph paths (TextPath/PathPatch) and TextToPath metrics, resolving rounding jitter (fixes Issue #35). Supports multi-line curved labels and auto-flipping for bottom-half readability.
  • Wikipedia Rate-Limiting Fixes (PR #120): Resolved build failures in docstring gallery examples by standardizing Wikipedia thumbnail sizes and adding proper User-Agent headers to requests.
• ageron/handson-mlp (Aurélien Géron's Hands-On Machine Learning book repository)
  • Polars Dataframe Integration (PR #41): Maintained a community-supported fork migrating the book's notebook exercises (all chapters) from Pandas to Polars dataframes, including custom Polars tools and database connection guides.

Project	What it shows	Links
Repeated sprint training trial (research)	Statistical rigor, reproducible analysis artifacts	Open Access Paper Published in Science and Medicine in Football Data & Code Repo
Drill Design App (production)	Product thinking, data modeling, shipping a real app	https://github.com/PGupta-Git/case-study-drill-design-app https://www.drilldesignapp.com
Off-ball movement metric & player similarity (Premier League)	Metric design from raw tracking data, dual-signal similarity framework, structural-null semantics, sensitivity checks, visual storytelling	https://github.com/PGupta-Git/case-study-btla-framework
Player availability & decision support	KPI redesign, feature engineering, time-aware validation, decision framing	https://github.com/PGupta-Git/case-study-player-availability-decision-support
Tactical / recruitment / performance analysis	Benchmarking, robustness checks, communication through visuals	https://github.com/PGupta-Git/case-study-tactical-recruitment-performance-analysis
Open-data experimentation lab	Public code: clean DS workflow, evaluation, visual storytelling	https://github.com/PGupta-Git/open-football-experimentation-lab

Note: case studies are anonymised (organisation names and private code/data are omitted).

• Designed a novel geometric metric (BtLA) from 25Hz optical tracking data to profile a player's off-ball movement between lines, with continuous passing-lane openness scoring and segment-aware smoothing (see BtLA Framework Case Study).
• Built a dual-signal player similarity framework (cosine on movement shape + Euclidean on scaled level/volume) across a full season of Premier League event data, bridging tracking and event data paradigms (see BtLA Framework Case Study).
• Built non-linear models on high-frequency biometric telemetry to separate signal from noise and predict failure-mode risk (injury-risk proxy; see Player Availability Case Study).
• Engineered new "availability" features with domain experts, replacing legacy KPIs with more predictive signals (see Player Availability Case Study).
• Built forecasting models and automated reporting workflows, saving ~15 hours/week and reducing data retrieval latency by ~30%.
• Delivered executive dashboards for decision-making (market sentiment, product performance, performance monitoring; see Drill Design App and Tactical/Recruitment Case Study).
• Led cross-functional delivery (Agile/Scrum) and bridged data engineering and non-technical stakeholders.